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CHAPTER 2

LITERATURE REVIEW

2.1 INTRODUCTION

This chapter reviews the various works carried out by scientists and

researchers from different part of the world with respect to the importance of

minerals in the recent times, their origin, occurrence and abundance,

conventional and advance mineral exploration techniques, uses of satellite

sensing and image processing in mineral exploration and hyperspectral

remote sensing for studying and mapping bauxite, iron ore and limestone

resources.

The reviews have been done and presented under various topics

such as source, distribution, occurrence and type of bauxite, iron ore and

limestone, geological and geomorphic mapping for mineral exploration.

Apart from listing and reviewing in detail the research works in this

chapter, a detailed review on the study sites, mineral exploration and

mapping, image processing, multispectral and hyperspectral remote sensing

for mineral mapping and ground truth has been carried out and furnished in

the respective chapters.

2.2 IMPORTANCE OF MINERALS

The importance of minerals and metals in our daily life and in a

nation’s economy need not be over emphasized. Addressing the significance

22

of minerals, Driscoll (2004) poses the question: “Why are minerals so

important?” And he proceeds to mention that the answer is simple and it is

same as the slogan of the Industrial Mineral Association (IMA) Europe -

“Your world is made of them". The author further notes that without minerals,

a range of everyday domestic and vital industrial products would just not

exist. In an average 95 working days, he adds, we will probably come into

contact with at least 100 items that have been manufactured by minerals.

Yet another emphasis on the economic important of minerals to a

nation can be seen in a report entitled “the economic important of minerals to

the U.K” produce by the British Geological Survey for the office of deputy

prime minister (Mineral and Waste planning division), wherein it has been

clearly mentioned that minerals play a fundamental role in underpinning

growth in the economy and in contributing to the UK’s high standard of

living. Thus, there is a need for minerals and also a greater need for exploring

them with success.

2.3 MINERAL EXPLORATION

Sasaki and Ishihara (1985) opine that after the Second World War

the technology revolution put rapidly accelerating demands on mineral

resources. Getting more out of existing deposits and prospecting for new ones

then became the central issues. The era in which deposits were sought

patiently and painstakingly by looking for surface indications was over. The

key to exploration for virgin hidden ore bodies or new deposits in completely

undeveloped areas with no known deposits nearby thus became a

consideration of how the deposits in question might originate.

Giving a detailed account of the various aspects of mineral

exploration and mining geology and by using the concept and practices of

applied geology, Peters (1978) presents a balanced and comprehensive

23

treatment of the geological, geochemical, geophysical and economic elements

of mineral exploration. The author goes on to include advances in satellite

imagery, litho-geochemical survey, isotope geochemistry, computer

applications and other developments in the field of mineral exploration. This

literature has provided enough input to me to know better about mineral

exploration and the need for application of recent techniques for the same.

A detailed review of the applications of recent technique in mineral

exploration has been given by Bhasin (1998). The author mentioned that the

use of satellite imagery, generally a combination of PAN and multispectral in

mineral exploration has been used as a base for well-planned exploration

programmes over the last decade, especially with higher resolution imagery

available every year. Depending on the purpose of exploration there are a

number of airborne techniques used for determining possible target zones and

more often cutting exploration costs drastically by narrowing down the area to

be covered by detailed ground geophysical studies. Citing an example, the

author adds that the state of Orissa in eastern India now has an extremely

marketable and valuable commodity in terms of a digital database covering

75,000 sq. kms and incorporating, high resolution aeromagnetic, radiometric,

digital elevation, satellite imagery, topographical data, all collected and

collated at 1:50,000 scale. The advantages of such a program include:

(1) Lower field exploration and drilling costs, by identifying

target sites for effective use of field resources.

(2) Increase target quality and confidence by integrating all types

of data.

(3) Map and understand geological structures with interactive on-

screen interpretation and creation of geology maps.

(4) Print professional maps by producing maps for field use, for

management reporting and for investor relations.

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The author finally comments that countries such as Malaysia,

Indonesia, Australia and Africa have been generating large revenues from the

mining industry, perhaps due to their ability to adopt the technology that

combines remote sensing, geophysics and ground studies. Taking clue from

the article by Bhasin (1998), this research work carried out in this thesis has

also extensively used satellite imagery and ground based studies mapping,

sampling and geochemical analysis to study bauxite, iron ore and limestone

resources in certain parts of south India.

Marjoribanks (2010) discusses about geophysics and geochemical

methods of exploration and mentioned that: in prospective area where outcrop

is poor or that have been subjected to intense mineral search over a long

period of time the explorationist has to make use of geophysics and

geochemical method in order to extend the search into areas of shallow cover

in available to traditional geological prospecting. The author sites that two

types of geophysical and geochemical survey, The first type of survey is

mapping of the areal distribution of a particular rock or soil characteristic – it

could be, for example, patterns of electromagnetic reflectance, magnetic

susceptibility, rock conductivity or element concentrations/ratios in rocks

soils or drainage sediments. The second type of geophysical/geochemical

survey is aimed at measuring unusual or a typical feature of rocks that directly

reflect, and have close spatial relationships to, economic mineralization. Since

ore bodies are in most cases small relative to the earth’s crust, such surveys

have to be based on detailed, close-spaced measurements and are generally

expensive.

Having reviewed certain literature regarding conventional methods

for mineral mapping/exploration, it is necessary to know more about the

works carried out related to the origin, occurrences and composition of

bauxite, iron and limestone mineral deposits of Tamilnadu, India.

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2.4 BAUXITE, IRON AND LIMESTONE RESOURCES – A

REVIEW

Since this thesis is concerned with studies on bauxite, iron ore and

limestone, this section reviews the various studies related to origin and types

of the deposits, their exploration, uses and related aspects. In addition to the

description here, Chapter 3 also contains additional information about these

resources (such as occurrences in Tamilnadu)

The term bauxite is used for products rich in alumina but low in

alkalis, alkaline earth and silica. The term bauxite ore is applicable to bauxite,

which are economically mineable containing not less than 45-50% Al2O3 and

not more than 20% Fe2O3 and 3-5% combined silica. The term alumina refers

to pure Al2O3 containing 52.9% Al and 47.1% O (Valeton 1972).

According to Darwin (2005) bauxite is the primary ore of

aluminium and is a naturally occurring, heterogeneous mineral composed

primarily of one or more aluminium hydroxide minerals plus various mixtures

of silica, iron oxide, titanium, alumina silicate, and other impurities in minor

or trace amounts (Banerji 1982). Bauxite is a weathering product of

aluminous rock that results from intense leaching in tropical and subtropical

areas by a process called laterization (Lamb 2005). It has a wide range of

common uses and approximately 85% of the world bauxite production is

processed into aluminium. The principal aluminium hydroxide minerals found

in varying proportions with bauxites, gibbsite and the polymorphs boehmite

and diaspora. Bauxites are typically classified according to their intended

commercial application: abrasive, cement, chemical, metallurgical, refractory,

etc. (USGS 2007). This quickly growing demand has given rise to a

continuing search for bauxite all over the world.

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Kondalite is highly susceptible to alteration, especially under

tropical conditions in south India. Fresh kondalites are light coloured and

studded with specks of red or brown garnet in hand specimens. Under

microscope, it is seen to be composed of much quartz with subordinate

amount of orthoclase, andesine, garnet and comparatively small quality of

sillimanite, graphite, biotite and rutile. Orthoclase shows perthitic texture and

is occasionally associated with minute patches of micropegmaitites. The

garnet is porphyroblastic with inclusion of quartz and contains several minute

acicular intrusions. The sillimanite, mostly altered to hydrated oxide of Al, is

in the form of long, slender prisms or needles frequently disjoined, broken

across the length. On alteration, the feldspars are kaolinised and garnets are

limonitised. The sillimanite alters the hydrated oxide of Al, quartz is

eliminated by leaving behind small amount of free silica (Krishnan 1935).

Charnockite, on the other hand, consist of quartz, feldspar and hypersthenes

with or without garnet. On alteration, the plagioclase feldspar changes over

mostly to kaolin and rarely to gibbsite. Ferromagnesium minerals such as

pyroxene and garnet are replaced by oxide of iron.

Ramam (1978) studied bauxite derived from khondalite and

charnockite, and it is interesting to note that Al2O3 content is 42% to 61% in

bauxite derived from charnockite. It is quite probable that the charnockite

mentioned in this study are garnetiferous charnockite found adjoining to

khondalite belt. The bauxite profile derived from khondalite and garnetiferous

charnockites have vast areal extent and sizeable thickness. The bauxitization

process is mostly controlled by geomorphic features. The nature of weathered

product in the initial state depends on the composition of parent rock and as

bauxitization proceeds, geomorphic expression with favorable slope which

facilitate free drainage of the leachates is very important for the formation of

bauxite deposits. If there is no favorable slope to facilitate free drainage, there

27

can be a stagnation of water promoting ‘saprolitic weathering’ resulting in the

formation of residual clay (Loughnan 1962 and Keller 1956).

Iron is one of the most abundant metals and has the third highest

crustal abundance (5.6 %), next to aluminium (8.2%) and silicon (28.2%).

Iron accounts for more than 95% of all metals used by the modern society. In

fact, the industrial growth of a country is measured, amongst other criteria, by

the amount of iron consumption and steel production. The ore minerals from

which iron is extracted are hematite, magnetite and goethite. Iron smelting is

carried out by reducing iron oxides to iron metal by reaction with carbon

monoxide gas, usually derived from coke, Craig et al (1996).

Iron ores of magmatic, sedimentary and metamorphic origin are

found in different geological settings. Magnetite occurs associated with

layered mafic-ultramafic intrusions as magmatic segregations. Iron ores,

initially of sedimentary origin, are the ones which account for the largest

resource of the metal and are exploited extensively in the world. Lateritic iron

ores are prevalent in tropical humid regions over ferruginous bed rocks.

The iron ore deposits of India can be divided into four groups

according to their mode of formation. The most important group includes the

banded iron ores of Precambrian age. These deposits are the back bone of iron

and steel industry in India and their export to countries like Japan fetch a huge

amount of foreign exchange for the country. The total reserves are estimated

at over 17,000 million tons, of which 14,000 million tons represent haematitic

ores and the rest are magnetitic ores. These iron-ore deposits can be

considered under two main groupings: (a) those occurring within complexly

folded BIFs in high grade terrain in parts of Andhra Pradesh, southern

Karnataka, Kerala and Tamil Nadu, and (b) those confined to the Archean

schist (greenstone) belts in Jharkhand, Orissa, Madhya Pradesh, Maharashtra,

Goa, and Karnataka, accounting for the predominant iron resource of the

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country. The first group deposits are considered to be > 3000 Ma old whereas

the deposits of the second group formed during the period 2900 to 2600 Ma,

Radhakrishna et al (1986).

Extensive deposits of BMQ and BHQ occur in the hilly tracts of

Goa and Karnataka. In the latter state, prominent occurrences are found in the

Bababudan hills, at Kudremukh, in Bellary and Sandur. Proved magnetite

deposits are confined to the Chikmagalur district of Karnataka (Bababudan

and Kudremukh) and also in the high grade terrains of Salem and Vellore

districts of Tamil Nadu.

Carbonate rocks make up about one-fifth to one-quarter of all

sedimentary rocks in the stratigraphic record. They occur in many

Precambrian assemblages and in all geologic systems from the Cambrian to

the Quaternary. Both limestone and dolomite are well represented in the

stratigraphic record. Dolomite is the dominant carbonate rock in Precambrian

and Palaeozoic sequences, whereas limestone is dominant in carbonate units

of Mesozoic and Cenozoic age, (Ronov 1983).

On the basis of their abundance alone, about the same as that of

sandstones, carbonate rocks are obviously an important group of rocks. They

are important for other reasons as well. They contain much of the fossil record

of past life forms, and they are replete with structures and textures that

provide invaluable insight into environmental conditions of the past. Aside

from their intrinsic value as indicators of Earth history, they also have

considerable economic significance. They are used for a variety of

agricultural and industrial purposes, they make good building stone, they

serve as reservoir rocks for more than one-third of the world’s petroleum

reserves, and they are hosts to certain kinds of ore deposits such as epigenetic

lead and zinc deposits.

29

The microscopic study of carbonate rocks dates back to the

beginning of petrographic analysis. The science of petrography was initiated

by an English geologist named Henry Clifton Sorby, who began petrographic

analysis in about 1851 with the study of limestones. Other historically

interesting early studies of carbonate rocks include investigation of carbonate

sediments in the Bahamas by Black (1933) and Cayeux’s (1935) classic work

on the carbonate rocks of France. Modern study of carbonate sediments and

depositional processes is generally regarded to have begun in the 1950s with

the publications of Newel letal (1951), Illing (1954) and Ginsburg (1956)

dealing with modern carbonate sediments in the Bahamas and Florida Bay.

2.5 BAUXITE, IRON AND LIMESTONE MINERAL DEPOSITS

OF TAMILNADU

Tamil Nadu is endowed with several minerals of which a few are

exploited economically. The important minerals include fossil fuel lignite,

metalliferous minerals such as base metals, bauxite, chromite, gold, magnetite

iron ore, molybdenum and non-metallic and industrial minerals such as

apatite, rock phosphate, asbestos, barytes, clay, corundum, construction

material (dimensional stones), feldspars, gemstones, graphite, gypsum, heavy

mineral sand, limestone, magnesite, mica, ochre, moulding and glass sand,

quartz, sillimanite, steatite and vermiculite. Besides these, minor occurrences

of minerals such as beryl, celestite, columbite-tantalite, garnet, ilmenite,

kankar, nickel ore, pyrite, allanite and salt are also recorded by GSI (2006).

The present study focussed more on the occurrence of iron, bauxite and

limestone, thus the details of their occurrence and distribution is reviewed in

the following sections.

2.5.1 Bauxite occurrence

In Tamil Nadu, the potential reserve estimation for bauxite amounts

to 13.52 million tons of marginal grade with an alumina content of 45 -50%

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(Report of the task force on Mineral Resources 1972). This reserve estimation

is based on many assumptions and is confined to the highest peaks of the

Shevroy, the Kollimalai hills and the Nilgiris where lateritic bauxitization

have been noticed. On opening a mine at Yercaud, undesirable variations in

the distribution of bauxitised zones were noticed (Krishnan 1942). Therefore,

it is desirable to explore the bauxite deposits more carefully to avoid wastage

both in manpower and in exploiting bauxite deposits.

The distribution of lateritic-bauxite deposits of south India is very

interesting. Lateritic bauxite capping in the Nilgiris and Palni hills occur at an

altitude of 2134 m; at Shevaroy at an altitude of 1500 m and at Kollimalai

hills at an altitude of 1300 m (Krishnaswamy 1958). Though at first sight the

altitudinal differences suggest two independent landform surfaces, a synoptic

view based on Landsat imagery the authors suggest that the bauxite deposits

of these areas are remnants of a single, once extensive plantation surface with

gentle undulating topography. Lineament features have been studied

extensively in this region by Subramanian et al (1974). Mega lineaments and

minor lineaments in Shevaroy, Chitteri, Kalrayans, Kollimalai and

Pachaimalai hills have influenced the preservation of residual profiles. The

mega-and minor-lineaments have contributed to laterite-bauxite capping at

Kollimalai and Shevaroy hills (Mani 1977). It is interesting to note that the six

lateritic-bauxite capping-profiles in the Shevaroy and seventeen laterite-

bauxite capping-profiles in Kollimalai hills are roughly parallel to N300E

trend. They may be considered as zone of bauxitization (Sanjeevi 2008).

2.5.2 Iron ore occurrence

Iron and steel are the backbone for industrial development in a

country. The vitality of the iron and steel industry largely influences the

economic status of a country. Hematite and magnetite are the most important

iron ores in India. About 60% hematite ore deposits are found in the Eastern

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Sector. About 87% magnetite ore deposits occur in Southern Sector,

especially in Karnataka. Of these, hematite is considered to be superior

because of its high grade. Indian deposits of hematite belong to the

Precambrian Iron Ore Series and the ore is within banded iron ore formations

occurring as massive, laminated, friable and also in powdery form, (IBM

2007). Magnetite ores are generally restricted in Karnataka, Tamil Nadu,

Kerala, Andhra Pradesh, Goa, Rajasthan and Assam. Small resources have

been established in other States also. As per IBM (2006), total resources of

magnetite ore in the country is 10.61 billion tons of which reserve is only

58.50 million tones and the rest is resource. The iron ore formations of parts

of Tamil Nadu were studied by Krishnan and Aiyangar (1944), Saravanan

(1969), Anjaneya and Krishna Rao et al. (1970), Dymek and Klein (1988) and

Ali and Robert (2005).

A number of magnetite quartzite bands of variable thickness and

length are known to occur all over the state, especially in the area north of

Cauvery River. The deposit of Salem- Trichinopoly- Arcot region constitutes

the most valuable group of iron ore deposits in Tamil Nadu (GSI 2006).

Besides these, minor occurrences are also reported from other parts of the

state but most of them appear to be uneconomical. The major deposits are

located within Salem, Attur, Harur, Nammakal and Rasipur taluks of Salem

district and the Musiri and Perambalur taluk of Tiruchirapally district. The

iron ore region is hilly and divided into two unequal portions by the Attur

valley which extends in easterly direction from Salem towards Cuddalore

(Dubey 1943). The northern area comprises Kollimalai, Pachaimalai,

Talamalai and Bodamalai. The best-known deposits are found in the

Kanjamalai and Godumalai, both of which are comparatively small isolated

hills in western part of Attur (Krishnan 1944). Chemical analysis of ore

samples collected from different areas of Salem and Tiruchirapally district

show 35-40% Fe, 50% silica with little or no sulphur and phosphorous. The

32

estimate reserve up to a depth of 100 m from outcrop is about 257.13 million

tonnes.

The important magnetite quartzite deposits of Tamil Nadu occur in

Tirthamalai, Kanjamalai, Godumalai, Vellalagundam, Kollimalai and

Tattayyangarpettai, Tiruvannamalai and Thirthamalai hill regions in the

northern districts of Tamil Nadu (Holland 1892). These iron formation consist

magnetite and quartzite and associated with charnockite, granites, pegmatite,

garnetiferous pyroxene granulite, amphibolites, basic intrusive and

hornblende-gneisses. Saravanan (1969) stated that ore deposits of Kanjamalai,

Salem are of meta-sedimentary origin.

2.5.3 Limestone occurrence

Srinivasan (1974) reported that limestone in Tamil Nadu occurs as

crystalline and non-crystalline (amorphous) varieties besides corals. The bulk

of limestone deposits are found to the south of Moyar –Bhavani - Attur

Lineament and thus the southern districts form the limestone province. The

crystalline limestones of Precambrian age are mainly distributed in parts of

Salem, Tiruchirapally, Karur, Madurai, Virudhunagar, Ramanathapuram,

Nagapattinam, Tirunelveli, Tuticorin and Coimbatore Districts. The author

adds that the total reserves of crystalline limestone are 200 million tonnes of

‘Proved’ category and about 25-30 million tonnes of ‘Inferred’ category.

Non-crystalline limestones are located in parts of Tiruchirapally, Tirunelveli

and Tuticorin districts.

Earlier reported by Narayanaswamy (1944) and Narasimhan (1961)

mentioned that the total estimated reserve of non crystalline limestone is

about 670 million tonnes of both 'proved' and 'inferred' categories of which

650 million tonnes of Cretaceous age distributed in east while Tiruchirapally

District, while rest are of Tertiary age distributed in Tirunelveli District. In

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Tirunelveli and Tuticorin Districts, bulk of limestone for cement industry

comes from Ramayyanpatti, Talaiyuthu, Pandapalli, Sattankulam and

Eluvaramukki - Pidaneri and Kayathar areas. The total reserve is about

20million tonnes with average CaO 45%, MgO 6% and SiO2 8%. Reserves in

bands near Puvandi, Sivagangai District are estimated to be 0.5 million

tonnes. Three bands of good quality limestone ranging in strike length from

1.5 to 6.5 km and upto 75 m wide occur near Pandalkudi, Palavanattam and

Chinnayapuram of Virudhunagar District.

After reviewing certain literatures related to the origin, occurrences

and composition of bauxite, iron ore and limestone mineral deposits of Tamil

Nadu, India, it is understood that though reserves have been reported and

exploration is being done, there is a need to look for more deposits and assess

the grades using modern methods. Hence, we need to know more about the

work carried out with reference to the applicability of remote sensing and

hyperspectral remote sensing for mineral exploration.

2.6 REMOTE SENSING FOR MINERAL EXPLORATION

Mineral deposit mapping is essential for sustainable and eco-

friendly exploitation of natural resources. Chandrasekar et al (2001) illustrates

the potential of multispectral satellite data for exploration and mapping of

banded magnetite quartzite along Tamil Nadu coast. In order to focus on

mapping of mineral deposits along coastal area, standardized multispectral

analysis has been carried out by the authors using Landsat satellite data. The

selected endmembers are identified by comparing the spectral signatures with

United States Geological Survey (USGS) spectral library. Finally the

endmembers are mapped with spectral angle mapper (SAM). Ground

verifications performed to assess the accuracy of classification were mostly in

agreement with the obtained results.

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Visual Interpretation of LANDSAT imagery of Eastern Ghats-

Godavari delta area on scale 1:1,000,000 on spectral bands 5 and 7 was

attempted by Rao (1977). A neotectonic and geomorphic map was prepared

from these images showing among other features major and minor

lineaments. The study discussed the importance of lineaments for

sedimentation, oil migration and localisation of ore bodies.

Grover and Bakliwal (1985) have demonstrated that the soil and

vegetation masked metalliferous horizons can be picked up by special

processing of satellite data and in case of concealed deposit remote sensing

greatly helps in bringing out the lithological architecture, folded rythmicities,

lineament network and also the geographic panorama through which minerals

can be targeted.

In another example, Dogan (2007) studied the Tokya provenience in

Turkey using Landsat enhanced thematic Mapper images. The author

prepared different ratio maps like ferrous mineral abundance map, iron oxide

map and NDVI map. The produced ratio maps were transformed in to raster

index maps. From the NDVI map, the NDVI values from 136 to 225 were

classified as dense forest and this area were masked from the prepared index

maps using natural breaker method and the produced index maps were

classified in to nine classes. The results of this work have much relevance to

the work reported in this thesis as similar ratioing techniques have been used

by me to study iron ore deposits in Tamil Nadu, South India.

Woldai et al (2006) had chosen Magondi Belt Zimbabwe for

mineral potential mapping using the favourability functions approach. The

datasets comprised of an old geological map, a detailed airborne total

magnetic field survey, and geochemical samples at the nodes of an

exploration grid, have been integrated using seven different inference

techniques through the joint probability function under the conditional

35

independence hypothesis. Using sensitivity analysis of the favourability

functions allowed evaluating the most important factors controlling

mineralization occurrence, and thus worth additional future investigation.

The Pulang porphyry copper deposit in SW China was studied by

Wen and Han (2009) by the integration of data sets in to a GIS and analyzed

using Arc-SDM software. Data like geologic, geochemical, geophysical,

remotely sensed datasets (like EO-1 Hyperion hyperspectral remote sensing

data and Landsat ETM+ imagery) and deposits training data were used for the

analysis. Arc-SDM is a collection of geoprocessing tools for spatial data

modelling using weights of evidence, logistic regression, fuzzy logic and

neural networks. Finally, mineral potential mapping was generated using

weights of evidence model. Some new targets located at mineral potential

mapping were also validated, thus demonstrating the success that can be

achieved by an integrated approach of mineral exploration.

In another example of an integrated study, airborne magnetic,

ASTER and Landsat Thematic Mapper images were used by Tessema et al

(2012) for the detection of detailed exploration targets for kimberlite pipes

located around the Kimberley and Boshof regions in the Northern Cape

Province of South Africa. Magnetic data and satellite images were processed

to identify new potential targets of kimberlite. Based on this approach, 30

kimberlite-like bodies were identified, of which 11 were given high ranks for

detailed exploration. The ranking was based on a comparison of the strength

of magnetic intensity and the size and geometry of the magnetic signatures. In

addition, the spectral angle mapping (SAM) method was applied to the first

nine ASTER bands, and this enabled the authors to distinguish kimberlite

indicator minerals (ilmenite, serpentine, olivine and phlogopite). The study

says that the SAM technique was successful in supplementing the

identification of detailed exploration targets.

36

Zhang et al (2007) used ASTER data for evaluating the gold related

lithological mapping and alteration mineral detection in south Chocolate

Mountains area, California, U.S.A. The study compared different methods for

extracting mineralogical information from ASTER data, compared the

remotely derived maps to the mapped field geology, and used the ASTER

data to map minerals and lithologies related to gold exploration. For this, the

authors had used maximum likelihood classification method, band ratio, PCA

analysis and sub-pixel unmixing algorithms were used to detect significant

alteration minerals using the ASTER VNIR and SWIR surface reflectance

data and reference spectra from the ASTER spectral library. With the ASTER

data the authors were able to map the most favourable host rocks of gold

deposits producer's accuracy of 86%, and were also mapped in some areas

that were not shown on the field geologic map. Alteration minerals like

alunite, kaolinite, muscovite and montmorillonite were detected by sub-pixel

unmixing analysis of the ASTER reflectance data. The study shows that the

CEM technique is a powerful sub-pixel unmixing analysis tool for analysing

ASTER reflectance data.

Volesky et al (2003) of The University of Texas at Dallas conducted

a study to evaluate the utility of ASTER and Landsat ETM+ data for mineral

exploration using data sets covering the Wadi Bidah Mineral District, Saudi

Arabia. Remote sensing data were used in conjunction with GIS to map

lithological units and mineral deposits, analyze and define regional structural

trends, and establish geologic controls on sulfide mineralization. Authors used

colour composite images produced with combinations of bands and band

ratios are used to find, map and evaluate massive sulfide deposits found in the

Wadi Bidah Mineral District. The result indicates that the massive sulfide

deposits have a surface expression in the form of iron-rich caps (gossans) and

zones of hydrothermal alteration, all having distinct spectral signatures.

37

Structural control of mineralization appears to be limited to post-

mineralization folding and shearing.

In another study in a desert region, Madani and Emam (2011)

discriminated and mapped the basement rocks as well as the barite

mineralization exposed at El Hudi area, Southeastern Desert, Egypt using the

processed short-wave infrared bands of ASTER in collaboration with the field

verification and petrographic analysis. The metasedimentary rocks have gray

and dark gray image signatures on the ASTER band ratio image 8/5, which

correspond to biotite gneiss, migmatites, and hornblende biotite schists,

respectively. Presence of absorption feature near band 8 (2.295 – 2.365 m)

for the chlorite alteration product is probably responsible for the lowering of

the 8/5 band ratio value and the dark gray image signature exhibited by

hornblende biotite schists. On 7/8 band ratio image, Abu Aggag granites have

dark gray image signature whereas postgranitic dykes have white image

signature. Presence of absorption feature around band 7 (2.235–2.285 m) for

the kaolinite mineral may be responsible for the dark gray image signature

exhibited by Abu Aggag granites. Garnetiferous muscovite granites have gray

image signature on 5/4 band ratio image whereas pegmatites and postgranitic

dykes have black image signature. Barite veins can be distinguished within

garnetiferous muscovite granites by their dark gray image signature on 5/4

band ratio image. The spectral reflectance curve of barite exhibits absorption

feature around 2.1 m (band 5), which leads to lower the ratio value and

yields the dark image signature to barite veins. The above-described ASTER

band ratio images were integrated into one false-colour composite image (8/5:

R; 5/4G; and 7/8B) which was used to produce 1:100,000 geological map for

El Hudi area and to locate the barite mineralization.

Bhan and Hegde (1985) delineate target areas for mineral

exploration by visual interpretation in the northern and central part of Orissa

38

State, in eastern India. Image data used is digitally analysed and enhanced for

delineation of bauxitised lateritic plateau and for comparison with visual

interpretation. According to the authors, LANDSAT data have been found to

be very useful in redefining the tectonic structure, correlation of regional

features and mapping of hitherto unmapped features.

Applications of remote sensing and Geographic Information

Systems to assess ferrous minerals and iron oxide of Tokat province in

Turkey has been studied by Dogan (2008) using Landsat- ETM+ satellite

images. To avoid incorrect interpretations, dense plant or closed forest areas

were determined using the normalized difference vegetative index, and

excluded from the evaluation by masking.ferrous minerals and iron ore index

maps were produced using related algorithms and remote sensing tools.

Classification and spatial analysis operations were conducted under the

framework of geographic information systems. A natural breaks method was

employed for classification, and both indices were summarized in nine

classes. The relationship between two index maps was investigated using a

bi-variety correlation analysis. The correlation between two index maps

(0.549) was found to be significant at the 1% level. Developed index maps

were tested using ancillary data from previous studies in the area. The results

were summarized at the administrative district level.

Soe et al (2005) used ASTER and Landsat Thematic Mapper data

for the identification of iron oxide in the Tanintharyi coastal area, Southern

Myanmar. The authors used band ratioing method (VNIR B2/BI in ASTER

image and VNIR B3/BI and SWIR B5/B4 in Landsat image) and Principal

component analysis (PCA) to locate the iron oxide minerals. The study

concludes that remote sensing techniques or image processing method has

successfully used for the identification of iron ores deposits. This study also

helped in developing similar ratio images for iron ore studies (as in Chapter 5)

39

Mineral composite characteristics (ferrous minerals (FM), iron

oxide (IO), and clay minerals (CM)) of the Kelkit River Basin in Turkey were

investigated and mapped by Dogan (2009) using remote sensing (RS) and

geographic information systems (GIS) tools. Mineral composite (MC) index

maps were produced from three LANDSAT-ETM+ satellite images taken in

2000. Resulting MC index maps were summarized in nine classes by using

‘natural breaks’ classification method in GIS. Employing bi-variety

correlation analysis, relationships among index maps were investigated.

According to the results, FM and IO index maps showed positive correlation,

while CM index map is negatively correlated with FM and IO index maps.

Negative correlations between iron and clay variables suggested that the

dominant clay minerals of the study area might be smectite, illite, kaolinite,

and chlorite, which have little or no iron content.

Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM)

data have been successfully employed in the field of mineral exploration to

identify key minerals over arid and semi-arid terrains by Liu et al (2011). In

this study, the authors used masking technique to eliminate the negative

influence of vegetation and cloud and Crosta technique to identify the

diagnostic features of hydroxyl-minerals, carbonate-minerals and iron oxides.

The mineral exploration work was carried through by synthetic analysis of the

remote-sensing images, geochemical data and structures. Finally, areas with

high correlation between the occurrence of hydrothermal alteration and

presence of main faults and geochemical anomalies were considered as

mineral exploration targets worthy of further detailed exploration

programmes.

Principal component analysis (PCA) is an image processing

technique that has been commonly applied to Landsat Thematic Mapper (TM)

data to locate hydrothermal alteration zones related to metallic deposits. In a

40

study by Crósta et al (2003), the authors applied PCA to ASTER bands

covering the SWIR for mapping the occurrence of mineral endmembers

related to an epithermal gold prospect in Patagonia, Argentina. This allows

detailed spectral characterization of surface targets, particularly of those

belonging to the groups of minerals with diagnostic spectral features like clay

minerals, sulphates and carbonates. The results of the study illustrate

ASTER's ability to provide information on alteration minerals which are

valuable for mineral exploration activities and support the role of PCA as a

very effective and robust image processing technique for that purpose.

The Advanced Spectral Analysis (ASA) technique, one of the most

advanced remote-sensing tools, has been used by Pal et al (2011) for

identifying mineral occurrences over Dalma and Dhanjori, Jharkhand, India.

The identification of the extracted endmember spectra is obtained by

comparing with available pre-defined library spectra of USGS, John Hopkins

University (JHU) and Jet Propulsion Laboratory (JPL) spectral libraries.

Three techniques, namely Spectral Feature Fitting (SFF), Spectral Angle

Mapping (SAM) and Binary Encoding (BE), are used for the identification of

the collected endmember spectra. A total of six endmember spectra are

identified and extracted in the study area. Mapping of mineral occurrences is

carried out using the Mixture-Tuned Matched Filtering (MTMF) technique

over the study area on the basis of collected and identified endmember

spectra. Results of the study using the ASA technique ascertain that Landsat

ETM+data can be used to generate valuable mineralogical information.

Reflectance spectroscopy is a standard tool for studying the mineral

composition of rock and soil samples and for remote sensing of terrestrial and

extraterrestrial surfaces. Ramsey et al (2002), describe an automated methods

of mineral identification from reflectance spectra and give evidence that a

simple algorithm, adapted from a well-known search procedure for Bayes

41

nets, identifies the most frequently occurring classes of carbonates with

reliability equal to or greater than that of human experts. The authors

compared the reliability of the procedure to the reliability of several other

automated methods adapted to the same purpose. Since the procedure is fast

with low memory requirements, it is suitable for on-board scientific analysis

by orbiters or surface rovers.

Evaporate minerals are very important raw materials in very

different and broad industries for years. Serkan (2008) mapped out the

industrial raw materials by using remote sensing techniques of Ankara Bala

region, Turkey. Band ratio, decorrelation stretch, principal component

analysis and thermal indices are used in ASTER images in order to map

gypsum minerals. For gypsum minerals previously known Crosta method is

modified and by the selection of suitable bands and principle components,

gypsum minerals are mapped. For TIR indices previously known Quartz

index is modified as Sulfate index and used for gypsum mapping. The results

of these methods are checked at the field and from the areas where the results

show high anomalies, samples were taken for spectral and X-Ray analyses.

The result of the study shows that Crosta method and Sulfate Index methods

were the best among other methods.

Thus various approaches which include band ratios, spectral angular

mapping, subpixel and per pixel classifications and image fusion tried by

many authors were reviewed and it is learned that a single standardized

approach (SOP) is not possible to explore all minerals. Similarly, in the case

of bauxite, iron ore and limestone, there cannot be a single approach for all

the three minerals.

42

2.7 HYPERSPECTRAL REMOTE SENSING FOR MINERAL

EXPLORATION

Recent advances in remote sensing have led the way for the

development of hyperspectral sensors. Hyperspectral remote sensing, also

known as imaging spectroscopy, is a relatively new technology that is

currently being investigated by researchers and scientists with regard to the

detection and identification of minerals, terrestial vegetation, and man-made

materials and backgrounds (Clark 1999). Hyperspectral data sets are generally

composed of about 100 to 200 spectral bands of relatively narrow bandwidths

(5-10 nm), whereas, multispectral data sets are usually composed of about 5

to 10 bands of relatively large bandwidths (70-400 nm).

Leverington (2010) used Hyperion and Landsat TM data to

discriminate the sedimentary lithologies of Melville Island, Canadian High

Arctic. Although images with low spectral resolution can commonly be used

in the mapping of classes possessing distinct spectral properties.

Hyperspectral images offer greater potential for discrimination of materials

characterized by more subtle reflectance properties. This study investigated

the effectiveness of Landsat Thematic Mapper (TM) and EO-1 Hyperion data

for discrimination of lithological classes at eastern Melville Island, Nunavut,

Canada. TM data were classified using a standard neural-network algorithm,

and both TM and Hyperion data were linearly unmixed using ground-truth

spectra. TM classification results successfully discriminate between classes

over much of the study area, although with incomplete separation between

clastic and carbonate materials. TM unmixing results are poor, with useful

class separation restricted to vegetation and red-weathered sandstone classes.

Hyperion results effectively depict the fractional cover of end members,

although the abundance images of several classes contain background

abundance values that overestimate surface exposure in some areas. The

43

author says, for the study area and surface classes involved, noisy

hyperspectral data were found to be of greater utility than higher-fidelity

broadband multispectral data in the generation of fractional abundance images

for an inclusive set of surface-cover classes.

Lampropoulos et al (2000) present a new, computationally efficient

method for automatic mineral exploration, detection and recognition. The

automatic mineral homogeneous region separation algorithm developed by

A.U.G. Signals in cooperation with the Canadian Space Agency (CSA) using

AVIRIS data and mineral signatures from the Nevada's (US) cuprite site is

described in this study. The hyperspectral data and spectral signatures were

provided by the Canada Centre for Remote Sensing (CCRS). The algorithm is

able to successfully divide the image in regions where the mineral

composition remains constant. Hence, it can be used for reducing the noise in

estimating the abundance parameters of the minerals on a pixel-by-pixel

basis, for image region selection and hyperspectral image labeling for data

storage and/or selective transmission. Through the presented approach is able

to: a) divide a hyperspectral image into regions of adaptivity where pixel

unmixing algorithms are able to extract the abundance parameters with higher

degree of confidence, b) increase the signal to noise ratio (SNR) of the present

spectral signatures in a region and c) apply the proposed hyperspectral

homogeneous region separation for data reduction (hyperspectral image

compression). Experimental and theoretical results and comparisons/trade off

studies are presented in their work.

The spectral and spatial properties of ASTER (SWIR) data was used

to map detailed lithological and hydrothermal alteration related to copper and

gold mineralization (Amin 2011). The differentiation and identification of

phyllic zone are important for exploring porphyry copper mineralization as an

indicator of the high potential area with economical mineralization of copper.

44

In this way, the authors attempt to demonstrate how ASTER remote sensing

data can identify and discriminate the hydrothermal alteration zones and

lithological units in a regional scale. It is therefore concluded that remote

sensing techniques are viable options for geological applications, offering

reliable and relatively low cost methods, and could be utilized further to other

virgin regions for lithological mapping and for initial steps of mineral

exploration.

Bishop et al (2011) studied the mountainous region of Pulang,

China employed a two-step progressive approach, first to locate target areas

characterized by hydrothermal mineral alteration, using (ASTER), and

secondly, to attempt detailed mineral mapping using Hyperion. The principal

components and broad-band spectral analysis of ASTER led to the detection

of two target areas characterized by argillic alteration, iron-oxide- and

sulphate-bearing minerals. A focused hyperspectral study followed using

Spectral Angle Mapper (SAM) and Mixture Tuned Matched Filtering

(MTMF) techniques, which allowed mineral species to be discriminated and

mapped in more detail. The study concludes that a combination of broad-band

and hyperspectral approach is feasible and advantageous for mineral

exploration in remote areas where primary information is limited or

unavailable.

In contrast to the older generation of low spectral resolution

systems, such as the Landsat Thematic Mapper with only six "reflected"

bands, the new generation of hyperspectral systems enable the identification

and mapping of detailed surface mineralogy using "laboratory-grade"

spectroscopic principles (Clark et al 1990).

Hyperspectral image data sets acquired near Cuprite, Nevada, in

1995 with the Short-Wave Infrared (SWIR) Full Spectrum Imager (SFSI) and

in 1996 with the Airborne Visible-Infrared Imaging Spectrometer (AVIRIS)

45

are analysed with a spectral unmixing procedure and the results were

compared by Neville et al (2003). The nominal pixel centre spacings are 1.0

by 1.5 m for SFSI and 16.2 by 18.1 m were given for AVIRIS across track

and along track, respectively; the region imaged by SFSI is a small portion of

the full AVIRIS scene. Both data cubes have nominal spectral band centre

spacings of approximately 10 nm. The image data, converted to radiance

units, are atmospherically corrected and converted to surface reflectances.

Spectral end members are extracted automatically from the two data sets;

those representing mineral species common to both are compared to each

other and to reference spectra obtained with a field instrument, the Portable

Infrared Mineral Analyser (PIMA). The full sets of end members are used in a

constrained linear unmixing of the respective hyperspectral image cubes. The

resulting unmixing fraction images derived from the AVIRIS and SFSI data

sets for the minerals alunite, buddingtonite, kaolinite, and opal correlate well,

with correlation coefficients ranging from 0.75 to 0.91, after compensation for

shadowing and misregistration effects.

Quid et al (2009) took laboratory reflectance spectra of 18 rock

samples from the Precambrian basement of north east of Hajjah were

measured and analyzed using the instrument of FieldSpec3 with spectral

range 0.250–2.500 µm. The study aims to use the spectral reflectance of rocks

for mapping the mineral resources in the north east of Hajjah. The altered

system in the study area comprises of silicification, sericitification, oxidation,

clay minerals and carbonatization. Silicified alteration is not distinguishable

in the regions of Visible-Near Infrared (VNIR) and Short wave Infrared

(SWIR) of the electromagnetic spectrum, because of lack of diagnostic

spectral absorption features in silica in this wavelength. Although the

arsenopyrite and pyrite are wide spread in the whole study area their features

do not appear in any range of spectra because they exhibit trans-opaque

behaviour and often lack distinction in VNIR and SWIR. The entire spectral

46

reflectance curves of samples show alteration. Based on the examination of

laboratory spectra all samples in the study area show promise in the field of

mineral resources.

Zhang (2008) used integration technology of remote sensing

imagery and aerial radioactivity data to extract the anomaly information

which relate to Taoshan uranium deposits in South China. The study has

provided a new approach to the exploration of uranium resources. Based on

hyper spectral data mining techniques, field spectra data is used to study the

diagnosable spectral signatures of uranium mineralization factors and the

diagnosable spectra identification symbol is developed by the author.

Using laboratory spectra and Hyperspectral Remote Sensing, Zaini

(2009a) mapped Calcite-Dolomite to Assess Dolomitization Patterns of

Bédarieux Mining Area, SE France. The author used diagnostic absorption

features in the Reflectance spectra in SWIR and TIR is used for identification

of pure and mixture of calcite and dolomite. The calcite-dolomite ratio were

derived from laboratory reflectance spectra of synthetic samples of calcite and

dolomite mixtures as a function of grain size fractions, packing models from

loose to compact packing sample, and mineral contents with five different

weight percentage of calcite contents. The ratios showed that positions of

carbonate absorption band are nearly linear to the calcite content and this is

used for assessing dolomitization patterns. The study concludes that

dolomitization patterns in the study area were weakly identified by the

HyMap images as compare to the laboratory reflectance spectra of the field

samples, but the simple linear interpolation method based on spectral

absorption feature parameters revealed a greatly potential to map calcite and

dolomite.

Mineral mapping on the Chilean–Bolivian Altiplano using co-

orbital ALI, ASTER and Hyperion imagery has been carried out by Hubbard

47

and Crowley (2005). Hyperspectal data is used for calibrating Advanced Land

Imager (ALI) and Advanced Spaceborne Thermal Emission and Reflection

Radiometer (ASTER) images and produced a 13-channel reflectance cube

spanning the Visible to Short Wave Infrared (0.4–2.4 Am). The result of the

study states that high spectral resolution, low signal-to-noise Hyperion data

were only marginally better for mineral mapping than the merged 13-channel,

low spectral resolution, and high signal-to-noise ALI + ASTER dataset.

Neither the Hyperion nor the combined ALI + ASTER datasets had sufficient

information dimensionality for mapping the diverse range of surface materials

exposed on the Altiplano. However, it is possible to optimize the use of the

multispectral data for mineral-mapping purposes by careful data subsetting,

and by employing other appropriate image-processing strategies.

Resmini et al (2007) have mapped the areal distributions of the

minerals alunite, kaolinite, and calcite of the Cuprite mining district, Nevada

by applying constrained energy minimization (CEM) to remotely sensed data

from the Hyperspectral Digital Imagery Collection Experiment (HYDICE)

sensor, a 210 channel, 0.4 mm to 2.5 mm airborne hyperspectral imaging

spectrometer. According to the authors CEM is a powerful and rapid

technique for mineral mapping which requires only the spectrum of the

mineral to be mapped and no prior knowledge of background constituents.

Also the results obtained from other spectral image processing techniques like

linear spectral unmixing and principal components analysis is similar to CEM

result.

Airborne hyper spectral visible to short wave infrared (VNIR-

SWIR) HyMap scanner were used to collect the well-exposed panorama Zn-

Cu volcanogenic massive sulphide (VMS) prospect, east Pilbara, Western

Australia by Cudahy (2000). The HyMap-derived mineral alteration maps

clearly show the architecture and physicochemistry of the hydrothermal

48

convective system, which is well exposed in cross section over 30 Km in

strike length. The minerals identified and mapped from the HyMap data

include: Muscovite Topaz, Pyrophyllite, Kaolinite, chlorite, Epidote

amphibole, Rozenite, Hematite, Goethite and “Hydrated “iron oxides as well

as green vegetation and dry vegetation. The level of Tschermak substitution in

muscovite can also be measured and mapped accurately associated with the

“magmatic” fluids which can be discriminated from the white mica alteration

associated with the “sea water fluids.

Stamoulis et al (2001) carried out a large area survey of 32

contiguous airborne hyperspectral HyMap image of the Proterozoic Giles

Complex in the Musgrave Province of South Australia. The main impediment

to this mapping is the pervasive vegetation cover. Minerals identified in the

Hymap spectra include Fe-chlorite, Mg-chlorite, amphibole, well-ordered

kaolinite and kaolin associated with bound water. This mineralogy is

consistent with alteration of ferromagnesian host rocks, some of which is

constrained to a network of fractures associated with an apparent dextral fault

that parallels other structures mapped in the area. The result shows that new

mineralogical information is being provided by the airborne hyperspectral

data that will assist mapping and possibly mineral exploration.

Abulghasem et al (2011) had studied the iron ore deposits and its

occurrences in the western part of Wadi Shatti district, Libya. An integration

of Enhanced Thematic Mapper Plus (ETM+) images were used and processed

by using Maximum Likelihood supervised Classification image and band

ratio images to discriminate and delineate different lithological units in the

study area. Magnetic data were used to identify any magnetic response

especially in the south side of the area which is totally covered with sand

dunes to discover any probable occurrence of iron ore. The study is supported

with field study and geochemical investigation. The results showed that the

49

iron ore belt still extend from the west and southwest part under the sand

dunes. The magnetic data show a big anomaly located to the south of the

study area under the sand dunes which could be sign of iron occurrence. The

authors finally produced a new potential map for the new areas of iron ore

deposit.

Khaleghi and Ranjbar (2011) had done alteration mapping for the

exploration of porphyry copper mineralization in the Sarduiyeh area, Kerman

province, Iran, using ASTER Shortwave Infra-Red (SWIR) data. The ASTER

SWIR bands enabled the generation of maps designed to represent the

abundance of broad minerals such as kaolinite, muscovite and chlorite which

are important in the identification of hydrothermal alterations related to

porphyry copper mineralization. SWIR bands from ASTER with the

wavelength between 1.65 and 2.43 m have a good potential for mapping

hydrothermal alteration. The authors pre-processed the images with Internal

Average Relative Reflectance (IARR)) and used minimum noise fraction

(MNF) transformation. PPI was used to extract the most spectrally pure pixels

from multispectral images. Spectral analyses of the hydrothermal alteration

minerals of the study area were obtained by matching the unknown spectra of

the purest pixel to the U.S Geological Survey (USGS) mineral library.

Matched filtering (MF) was used to enhance the hydrothermal alteration

minerals of the study area including kaolinite-dickite, muscovite-sericite-

illite, and chlorite-epidote by using the spectra which obtained from PPI. We

have also used directed principal component analysis (DPCA) for enhancing

hydrothermal alteration. Propylitic and phyllic-argillic zones could be

separated which are important for porphyry copper exploration.

Discrimination of alteration zones using Spectral Angle Mapping

and Linear Spectral Unmixing of the ASTER data at Sarduiyeh area, SE

Kerman, Iran was studied by Tangestani and Hosseinjani (2008). Spectral

50

Angle Mapping (SAM) and Linear Spectral Unmixing (LSU) algorithms were

applied to map alteration minerals using the image spectra and the spectra

selected from USGS library. Spectra of the image were extracted using the

"spectral end-member selection" procedures, including minimum noise

fraction (MNF), pixel purity index (PPI) and n-dimensional visualization.

Linear Spectral Unmixing using the image spectra obtained reasonable results

and successfully discriminated pixels with highest proportions of alteration

minerals, around copper deposits; while the abundance values of end-

members selected from the USGS spectral library were not satisfied for

output pixels. The study concluded that outputs obtained from the SAM and

LSU algorithms were more reliable when using the ASTER image spectra in

comparison to using spectra from the USGS library. Furthermore, LSU and

SAM algorithms discriminated similar regions for each alteration zone when

using the image spectra.

Soea (2008) used ETM+ image data to identify and map lateritic

soil zones in the Phrae basin which is one of the largest intermountain basins

in northern Thailand. The lateritic soil zones were discriminated using band

ratio and principal component analysis. The lateritic soil detection images

were processed by band ratio (band 3 / band 1), principal component analysis

of bands 1 and 3, and principal component analysis of bands 1, 3, 4, and 5.

The results of these three indices were superimposed using GIS to define a

preliminary lateritic soil image of the study area. A threshold method was

used for converting a grey scale image into a binary image. Different

threshold values were used to find the most probable areas of lateritic soil

zones in the image. The threshold values were determined from a published

geological map and known lateritic soil areas with good exposure in the

image. The quality of the results was evaluated by the normalized difference

vegetation index.

51

Sanjeevi (2008), in a study had shown the potential of spectral

unmixing of hyperspectral satellite image data for targeting and quantification

of mineral content in limestone and bauxite rich areas in southern India. In

this study spectral unmixing of ASTER image data is used to delineate areas

rich in carbonates and alumina. The author also used various geological and

geomorphological parameters that control limestone and bauxite formation

were also derived from the ASTER image. The study found out 16 cappings

with the help of DEM derived from SRTM that satisfy most of the conditions

favouring bauxitization in the Kolli Hills. The sub-pixel estimates of

carbonate content in the limestone area of Ariyalur, south India, matches with

the geochemistry of the samples collected from the study area. The study

concludes that spectral unmixing of hyperspectral satellite data in the VNIR

and SWIR regions may be combined with the terrain parameters to, target and

estimate the limestone and bauxite deposits accurately.

Rock and soil that may contain naturally occurring asbestos (NOA)

were mapped in the Sierra Nevada, California by Swayze et al (2009) using

the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). The use of

linear-mixture spectra calculated from spectra of dry grass and serpentine

allowed detection of serpentine in some parts of the study area with up to

~80% dry-grass cover. Chaparral vegetation, which was dominantly, but not

exclusively, found in areas underlain by serpentinized ultramafic rocks, was

also mapped. Overall, field checking at 201 sites indicated highly accurate

identification by AVIRIS of mineral (94%) and vegetation (89%) categories.

Zanbagh et al (2008) studied the application of matched filtering

technique to target alteration minerals. Matched Filtering (MF) technique

maximizes the response of the known endmember and suppresses the

response of the composite unknown background. In this paper MF technique

is applied to Landsat ETM+ data. First DN (Digital Number) values of

52

Landsat image have been converted to reflectance by atmospheric correction.

Then specific alteration minerals spectra’s (e.g. Iron oxide and clay minerals)

have been collected as endmembers and by applying MF method, these

minerals have been enhanced. The result illustrates that alteration mineral

which are advantageous for mineral exploration activities can be mapped with

such techniques.

Hydrothermal alteration zones have significant role in prospecting

of epithermal mineral deposits. Dehnavi et al (2010) had done image

processing of remotely sensed data for investigating hydrothermal alteration

zones in east of Kurdistan in Iran. The authors assessed the effectiveness of

ETM+ data for detecting alteration zones. Three band colour composite

images of ETM were produced based on optimum Index Factor method and

known spectral reflectance properties of rocks and alteration minerals. The

result of the study shows that colour composite of ETM bands (5, 3, 1)

achieved the most effective method for separation of hydrothermal alteration.

Kratt et al (2006) demonstrated the effectiveness of using a field-

portable ASD Fieldspec spectroradiometer, and satellite-based ASTER

imagery for mapping borate minerals in the Great Basin of the western United

States. Using the ASTER imagery reflectance characteristics of tincalconite in

the 0.4-2.5 m wavelength region as a guide, remotely generate mineral

abundance maps were made for Rhodes, Teels, and Columbus Marshes

(playas), located in western Nevada. Field observations confirmed the

presence of borate evaporates crusts in each of these locations and chemical

analyses of well, spring and groundwater samples suggest the possible

presence of hidden subsurface geothermal reservoirs.

Hyperspectral data coverage from the EO-1 Hyperion sensor was

used by Hubbard (2005) in calibrating Advanced Land Imager (ALI) and

ASTER images of a volcanic terrain area of the Chilean-Bolivian Altiplano.

53

The authors co-registered the ALI and ASTER datasets after calibration and

joined to produce a 13-channel reflectance cube with Visible to Short Wave

Infrared (0.4-2.4 μm). Eigen analysis and comparison of the Hyperion data

with the ALI + ASTER reflectance data, as well as mapping results using

various ALI+ASTER data subsets, provided insights into the information

dimensionality of all the data. In particular, high spectral resolution, low

signal-to-noise Hyperion data were only marginally better for mineral

mapping than the merged 13-channel, low spectral resolution, high signal-to-

noise ALI + ASTER dataset. The results shows, neither the Hyperion nor the

combined ALI + ASTER datasets had sufficient information dimensionality

for mapping the diverse range of surface materials exposed on the Altiplano.

However, it is possible to optimize the use of the multispectral data for

mineral-mapping purposes by careful data subsetting, and by employing other

appropriate image-processing strategies.

Hyperspectral remote sensing is used to discriminate and extract the

information of rocks or ore deposits,Gan et al (2000). According to the author

principal composite analysis gains better result at the Hougou gold deposit

region than other techniques. The Principal Composite Analysis is very

effective to extract the information of different rocks using hyperspectral data.

The first component (PC1) of PC transformation contains more spectral

information than others. Suitable RGB composite used PC1 and combined the

result of SAM, and then contrast enhancement. It can suggest and show the

based rocks, altered rocks and ore deposits. The author concludes that this

technique which is based on object-faced full spectral shape feature to

discriminate the information for rocks or ore deposits.

The complex epithermal gold system of Los Menucos District, Rio

Negro, Argentina, was mapped and explored by Kruse et al (2006) using a

combination of field mapping and multispectral/hyperspectral remote sensing.

54

Standardized analysis methods consisting of spatial and spectral data

reduction to a few key endmember spectra provides a consistent way to map

spectrally active minerals. Minerals identified using the JPL Airborne

Visible/Infrared Imaging Spectrometer (AVIRIS) includes hematite, goethite,

kaolinite, dickite, alunite, pyrophyllite, muscovite/sericite, montmorillonite,

calcite, and zeolites. Hyperspectral maps show good correspondence with the

results of field reconnaissance verification and spectral measurements

acquired using an ASD field spectrometer. Further analysis of Hyperion

hyperspectral data also indicates that similar mapping results can be achieved

from satellite altitudes.

Based on the literatures reviewed above, it may be inferred that

hyperspectral remote sensing is an effective approach to map and explore

certain mineral deposits compared to the inadequate multispectral studies.

2.8 IMAGE PROCESSING TECHNIQUES FOR MINERAL

EXPLORATION

2.8.1 Fusion

A general definition of image fusion is the combination of two or

more different images to form a new image by using a certain algorithm (that

is, with the availability of multi-sensor, multi-temporal, multi-resolution

image data from operational earth observation satellites). According to Hall

(1997), data fusion techniques combine data from multiple sensors, and

related information from associated databases, to achieve improved

accuracies and more specific inferences than could be achieved by the use of

a single sensor alone. Fusion of digital image data has become a valuable tool

in image evaluation. The aim of image fusion is to integrate complementary

data in order to obtain more information than can be derived from single

sensor data alone.

55

Co-registration of the source images is regarded as an important pre-

processing stage in image fusion. Zitova and Flusser (2003) describe the

various processes involved in image registration, with feature detection,

feature matching, and transform model estimation and image resampling. The

study involves manipulation of pixel values and thus pixel-level image fusion

is adopted, it requires the source image to be in perfect registration. Mis-

alignment of the images might result in errors or artificial features. Wehn et al

(1995) discuss the need for accurate co-registration of remotely sensed images

especially for data fusion and change detection studies. The authors put forth

an automated co-registration mechanism for tackling the differences in

images, namely, noise prior to co-registration them. This helps in perfectly

aligning the images which may otherwise result in variations in the fused

images. The system uses Landsat TM images for the study and the

registration accuracy is found to be 0.3 pixels. The images used for this study

has co-registered before further analysis.

Krishanamurthy (1990) reported that digital enhancement

techniques such as linear contrast stretching, the generation of FCC and PCA

of IRS_IA data, enabled the discovery of an appreciably large patch of

Deccan Trap basic volcanic rocks in Uttar Pradesh, India. Ramasamy and

Bakliwalin (1985) prepared a geomorphologic map of parts of central

Rajasthan, India, from digitally enhanced Landsat MSS data and suggested

the details of the zone of structural hills, structural valleys, and the structural

ridge.

2.8.2 Band Ratioing

In northern Chile, TM ratio images defined the prospects that are

now major copper deposits at Collahuasi and Ujina. Hyper spectral imaging

systems can identify individual species of iron and clay minerals, which can

provide details of hydrothermal zoning. Silicification, which is an important

56

indicator of hydrothermal alteration, is not recognizable on TM and hyper

spectral images. Quartz has no diagnostic spectral features in the visible and

reflected IR wavelengths recorded by these systems. Variations in silica are

recognizable in multispectral thermal IR images, which is a promising topic

Sabins (1999).

In the article, ‘Digital image processing of satellite imagery data for

mineral exploration,Ramasamy (1988) mentions about advantages of the

techniques like stretching, density slicing, normal and contrast stretched

colour composites, ration and ratio colour composites.

Most of the above mentioned applications of fusion were concerned

with generation of higher quality thematic maps using multi-sensor images. In

this thesis, a recent approach of image fusion is proposed for the identification

and mapping of magnetite bands in Kanjamalai hills.

2.8.3 Sub-pixel classification for mineral exploration

The idea behind classification of remotely sensed images is to

categorise the pixels into land cover classes based on their spectral

composition. Many classification algorithms exist including maximum

likelihood classifier (MLC), minimum-distance-to-means classifier (MDM),

and the parallelepiped classifier, of which MLC received greater attention.

The advantages of sub-pixel classification over conventional classifiers have

been understood from the following section.

A maximum likelihood classification was performed on Landsat 3

image data to study the status of forest degradation in the states of Uttar

Pradesh and Madhya Pradesh, India by Kachhwaha et al (1990). The accuracy

obtained by MLC is higher compared to other methods of classification. A

vegetation map depicting various species helped in assessing the degradation

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of forest with relatively less time and low cost using digital data. However,

the presence of mixed spectral signature (and hence, mixed pixels) resulted in

mis-classification. From this study, the presence of mixed and hence, the need

for mixed pixel classification is realised.

Panigraphy et al (1991) used MLC for rice acreage estimation in the

state of Orissa, India. For this, a set of images from various sensors such as

Landsat MSS and TM, LISS-I were classified using a stratified random

sampling. Classification results obtained gave an overall accuracy of 90%,

which suggests the significance of MLC although higher resolution data like

LISS-II would have resulted in much accurate acreage estimation of the state.

Foody (2002) discusses the key considerations that should be taken

into account while classification and accuracy assessment. The author reviews

the current status of accuracy assessment of land cover classification and lists

the difficulties encountered during accuracy assessment and its influence on

the same. The author concludes that the current status needs to be improved

so as to overcome the discussed difficulties and a single, universally

acceptable, standard measure of accuracy assessment should be designed for a

reliable evaluation of classifier performance.

Van Genderen et al (1978) address the problems in the sampling

strategies that are in practice. The authors suggest a new, reliable method

based on stratified sampling for determining the minimum sample size which

should be at least 30 numbers for each land cover category. This was tested

on Landsat MSS data of Murcia Province, South East Spain. The whole

method arises from the concept of incorporation of the probability of making

incorrect interpretation rather than the usual expression of interpretation

errors as percentage of number of sites.

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Since per-pixel classification of the data does not provide reliable

results, there is an alternative school of thought to opt for high spatial,

spectral resolution data, which considerably reduces the classification error.

Many researchers have discussed the importance of high resolution satellite

image data for better mapping.

Since multiple spectral categories occur within a pixel, it is not

logical to allot a single pixel, a single land cover class. In the case of ‘hard’

classification, an imprecise and false result is obtained since pixels do not

occur discretely. Pixels in reality inter-grade gradually between classes

without any sharp boundaries. Mixed pixels at boundaries are assigned to the

most similar class and much of the original spectral information can no longer

be retrieved from the classified image. Hence, they tend to reduce

classification accuracy. The demerits of the per-pixel classification were

overcome by the concept of spectral unmixing and many of the researchers

have done notable work on the same, which are as follows:

Liangrocapart and Petrou (1998) discuss in detail, the two

approaches in spectral unmixing namely linear and non-linear mixing. Under

controlled laboratory conditions, a comparative study of the approaches was

carried out. The responses of white and colored chalk powder were captured

using a digital camera. Linear unmixing was performed with and without the

use of chromaticity transformation (used to normalize the intensity in 3D

colour space). Nonlinear unmixing was used to compute bi-directional

reflectance. Thus, the authors mention about the two methods of spectral

unmixing in detail and give an overall description of the two methods.

Borel and Gerst (1994) have shown that non-linear spectral mixing

occurs due to multiple reflections and transmission from surfaces. The authors

present radiosity models for demonstrating multiple scattering and computing

vegetation indices and spectral BRDF. A model for rough surfaces was also

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described and the validation of the experiment was carried out using a

mechanical model, which agreed well with results obtained.

Another study by Ray and Murray (1996) using the field spectra of

plants in the Manix basin area of Mojave Desert was carried out to prove the

existence and importance of non-linear unmixing. The authors also studied

the change in the relative abundance when there is a change in the soil-plant-

background combinations. It was also deduced that non-linear mixing makes

vegetation more detectable while it also makes assessments of vegetation

much more difficult.

Though the non-linear mixing models the Earth’s surface better than

the linear mixing, it is a complex phenomenon and presents a number of

difficulties due to lack in simplicity of the model and its inability to account

for the multiple scatterings across each surface of land cover classes of the

image (scene variability). Therefore, linear mixing which has been used in

this study has an edge over non-linear mixing.

Shimabukuro and Novo (1997) proposed a methodology viz.,

mixing model for mapping of flood habitats in the Amazon basin. Two

adjacent scenes of Landsat TM available in digital format were used for the

study. The transformation of digital numbers to spectral reflectance values

and radiometric rectification was carried out on the images. Assessment of the

rectification process and the consequent application of mixing model yielded

a classified map of the flood habitats. Three endmembers were chosen as

input for the mixing model and the map thus obtained was compared with a

reference map derived from visual interpretation. Mapping using mixing

model yielded a good classification result. Though accuracy assessment was

not carried out, the paper proposes a new methodology namely, mixing model

for the purpose of mapping flood habitats to overcome the limitations of per-

pixel classification.

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The mapping of land cover components, vegetation, in particular,

with the help of NOAA-AVHRR (Advanced Very High Resolution

Radiometer) data was discussed by Shimabukuro et al (1997). Fraction

images of vegetation, soil and water/shade were derived from a set of six

AVHRR images in the Sao Paulo state, Brazil using linear mixing model.

Constrained least squares approach was employed and a global vegetation

cover map was available for comparison. In addition, NDVI values were

computed and there was a good correlation between the NDVI values and the

vegetation fraction obtained through unmixing. This paper gives an insight

into the use of linear mixing model for mapping vegetation cover. Though the

aim of the paper was to extract vegetation details, it brings out the importance

of soil and shade fraction images, which aid in a better understanding of the

spectral response of the other land cover types.

The comparison of success and the accuracy of three thematic

classifiers were attempted by Bastin 1997. The generation of simulated

images from Landsat TM data to different resolutions such as 3 3, 5 5, 7 7,

9 9 and11 11 windows was carried out by applying two filters namely,

simple mean filter and cubic filter. After the generation of images, the author

classified them at sub-pixel level using Fuzzy C-means classifier, Linear

mixture model and posteriori probabilities from Maximum likelihood

classification. The author has taken the classified (unsupervised) image of

original data to be the ‘ground truth’ information since accurate survey data

was not available. The partial values obtained from three classifiers were

compared with ground truth. It was found that FCM gave good estimates

closely related to ground truth. LMM correlated well with ground truth. The

probabilities from MLC gave inaccurate estimates. Also, the type of filtering

affected the accuracy. The author also suggests that each classifier has its own

ideal scale and set of signatures. Their suitability for specific applications,

however, needs to be experimentally determined. One drawback of the study

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is the lack of ground truth information, which forms the basis of comparison

between the three classifiers and true estimates of the land cover components.

Bateson and Curtiss (1996) suggested a method for selection of

endmembers, Manual Endmember Selection Method (MESM). MESM is a

multidimensional visualisation technique for interactively exploring the

mixing space in search of spectra to designate as endmembers. The method

makes use of parallel coordinates for endmember selection, which selects

endmembers according to the dimensionality of the data given as input. The

authors also discuss the various methods used to select endmembers and

compare MESM with the other methods listed. The method was tested on

AVIRIS data at the Konza Research Area, Manhattan, Kansas. Significant

correlation was found between unmixed abundances of vegetation, litter and

ground measures.

Bajjouk et al. (1998) used Principal Component Analysis (PCA) and

a linear programming method for the extraction of endmembers while trying

to apply the concept to coastal zone of Roscoff in France for the estimation of

seaweeds proportions. The authors used CASI data and based on the channel

selection (i.e., spectral discrimination), the number of endmembers varied

from four to six components. An affine algorithm was applied for linear

programming and the fraction images were classified. An accuracy

assessment of the fraction images resulted in overall accuracy of 85%. The

authors have shown that since endmembers are pure, they are located at the

extremes of the PC scatter plots. This study has demonstrated the method of

the selection of endmembers from PCA and also discusses the advantages of

principal component analysis. The authors have also pointed out that

endmember selection is dependent on the spectral richness of the image data.

The importance of the selection of endmembers for linear mixture

modelling was dealt by Mather and Koch (1997). Landsat TM image data for

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Los Monegros, Spain was subjected to PCA to determine the dimensionality

of the data after the masking of vegetation and water (lagoons in this study)

for deriving the soil endmembers and also masking soil and water for the

extraction of vegetation endmembers. It was found that fractions varied from

negative values to a value in excess of 1.0. Therefore the linear mixture

modelling did not fit into the data despite appropriate location of

endmembers. The mis-fit of the model may be attributed to the complexity

existing in the landscape and possibly due to the masking of land cover

components.

Emphasizing the significance of image endmembers and the scene

model, Milton (1999) utilizes CASI image data with spatial resolution of 4m

and its degraded image with resolution of 36m. Using both the image, the

author related the spatial resolution and the image endmembers. The intrinsic

dimensionality of the data was determined using MNF transforms.

Endmembers were selected using principles of convex geometry. It was

shown that endmembers derived from high resolution data were more

representative than the low resolution data. This study, thus, gives an insight

into a meaningful scene model obtained from high resolution data, which

forms the framework for endmembers.

Plaza et al (2003) developed H-COMP, an IDL based software

toolkit for visualisation and interactive analysis of the results provided by

endmember selection. This work introduces the evaluation strategies for the

endmembers derived using standard algorithms from hyperspectral images.

The toolkit evaluates on the basis of the availability/non-availability of the

corresponding ground-truth information either as a spectral library of

endmember signatures or as a series of fractional abundance maps of each

constituent material. It is also capable of generating simulated images and

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quantitatively comparing the various standard algorithms used for deriving

endmembers.

The identification of image endmembers is a crucial task in

hyperspectral data exploitation. Endmember extraction algorithms from

hyperspectral images were studied by Pablo et al (2006). Space-borne sensors

are currently acquiring a continual stream of hyperspectral data, and new

efficient unsupervised algorithms are required to analyze the great amount of

data produced by these instruments. Once the individual endmembers have

been identified, several methods can be used to map their spatial distribution,

associations and abundances. Authors had used Pixel Purity Index (PPI), N-

FINDR and Automatic Morphological Endmember Extraction (AMEE)

algorithms developed to accomplish the task of finding appropriate image

endmembers by applying them to real hyperspectral data. In order to compare

the performance of these methods a metric based on the Root Mean Square

Error (RMSE) between the estimated and reference abundance maps is used.

2.8.3.1 Applications of spectral unmixing

Quarmby et al. (1992) discuss the use of spectral unmixing viz.,

linear mixture modelling for crop area estimation. Multi-temporal AVHRR

dataset of 9 dates of Northern Greece were used and the input to the model

was obtained using supervised classification of SPOT HRV images. The

proportions of maize, rice, cotton and wheat were correlated with official

statistics from 18 village units for comparison of area of each category and

the accuracy was 89% showing the performance of the mixture model.

However, it is opined that a better method of input to the model would have

resulted in high and accurate estimates.

Thomas et al (1996) discuss the use of goodness-of-fit method for

the estimation of woodland using spectral unmixing. The author

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emphasizes that the use of endmember spectra for unmixing needs a

greater attention as unrepresentative spectra would degrade the quality of

the mixing model and hence, yield false estimates. Also, the author quotes

that the difficulty involved in endmember choice is due to the fact that they

vary at spatial scales. This statement needs to be reiterated for the

appropriate selection of endmember spectra.

The use of spectral unmixing as a tool for bauxite and laterite

mineral targeting and mapping in the Koraput district, Orissa, India was

carried out by Das (2002). Endmembers were chosen using the PPI technique.

The characteristic laterite cappings in the hills containing bauxite was

observed in the Landsat TM image and such region is generally devoid of

vegetation cover. MTMF, which performs partial unmixing based on the

endmembers supplied by the users, was used to unmix the abundances of

laterite/bauxite, vegetation and red soil. Chemical analyses carried out on

field samples confirm the presence of bauxite and laterite ores. This study has

helped in the discrimination of similar regions in the nearby hills of the

Koraput town.

Thus it is observed that many researchers have attempted and

highlighted the need of subpixel classification approach and selection of

accurate end member for spectral unmixing in the field of mineral

exploration. This concept is presented and explained in Chapters 4, 5 and 6.

2.9 HYPERSPECTRAL REMOTE SENSING TO EXPLORE

BAUXITE, IRON AND LIMESTONE

Remote sensing is the science of acquiring, processing, and

interpreting images and related data, acquired from aircraft and satellites,

which record the interaction between matter and electromagnetic energy.

Sabins (1999) describes the role of remote sensing in mineral exploration,

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according to him remote sensing images are used for mineral exploration in

two applications: (1) map geology and the faults and fractures that localize

ore deposits (2) recognize hydrothermally altered rocks by their spectral

signatures. Landsat thematic mapper (TM) satellite images are widely used to

interpret both structure and hydrothermal alteration. Digitally processed TM

ratio images can identify two assemblages of hydrothermal alteration

minerals; iron minerals, and clays plus alunite. In northern Chile, TM ratio

images defined the prospects that are now major copper deposits at Collahuasi

and Ujina. Hyperspectral imaging systems can identify individual species of

iron and clay minerals, which can provide details of hydrothermal zoning.

Silicification, which is an important indicator of hydrothermal alteration, is

not recognizable on TM and hyperspectral images. Quartz has no diagnostic

spectral features in the visible and reflected IR wavelengths recorded by these

systems. Variations in silica content are recognizable in multispectral thermal

IR images, which is a promising topic for research.

Vitorello and Galvao (1996) had reviewed the spectral properties of

geologic materials in the 400- to 2500 nm range for mineral exploration and

lithologic mapping. Only two major spectral intervals are widely used in

geological applications. In the first one, ubiquitous Fe2+ and Fe3+ provide

broad band absorptions in the 400- to 1100-nm portion of the spectrum related

to electronic transitions. Orbital multispectral scanners (eg, TM/Landsat)

supply broad-band images in the same spectral range of the electronic

absorption processes and, thus, have been extensively employed in the

discrimination of iron oxide-bearing surface materials. In the second interval,

narrow absorption bands occur in the range from 2000- to 2500-nm due to

vibrational processes related to Al-OH, Mg-OH, Fe-OH, and CO3.

Identification and even discrimination of carbonate-bearing rocks and

alteration clays require data collected by sensors with relatively high spectral

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resolution, such as the one offered by improved airborne hyperspectral

scanners (eg, AVIRIS).

Hyperspectral remote sensing has the potential to provide the

detailed physico-chemistry (mineralogy, chemistry, morphology) of the

earth’s surface Rajat et al (2010). This information is useful for mapping

potential host rocks, alteration assemblages and mineral characteristics, in

contrast to the older generation of low spectral resolution systems. Rajat et al

(2010) has used EO-1, Hyperion data for the delineation of AL+OH minerals.

The authors used cross track illumination correction and the log residual

calibration model to reduce the data noise and for extracting the extreme

pixels for finding bauxites from Hyperion images. Some pure pixel

endmember for the target mineral and the backgrounds were used in account

for the spectral angle mapping and matched filtering and the results were

validated with respect of field study.

Spectral properties of rocks can be considered as a tool to recognise

and discriminate different lithological units of an area by remotely-sensed

data, (Younis 1997). Nevertheless, physical and chemical natural processes

produce changes that modify to a considerable extent the mineralogical

composition of the rock surface (weathered surface) which mask some of the

spectral properties of the original surface (fresh surface). The author studied

various rock types (gypsum, carbonate, sandstone, lamproites, phyllite, and

quartzite) from a semi-arid region (SE Spain), and their bidirectional

reflectance factors were measured under laboratory conditions over the

spectral region between 400 and 2500 nm. The study reveals that reflectance

differences between the fresh and weathered surfaces (in brightness and

presence of characteristic absorption features) are highly significant in

spectral region and the effect introduced by the iron oxides are the most

important.

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Gersman et al (2008) had used EO-1 Hyperion scene to identify and

map hydrothermally altered rocks and a Precambrian metamorphic sequence

at and around the Alid volcanic dome, at the northern Danakil Depression,

Eritrea. Mapping was coupled with laboratory analyses, including reflectance

measurements, X-ray diffraction, and petrographic examination of selected

rock samples. Despite the difficulties, lithological mapping using narrow

spectral bands proved possible. With the help of spectral signature author’s

detected ammonium in the laboratory measurements of hydrothermally

altered rocks from Alid, which was confirmed by previous studies. Spectral

information of endmember’s mineralogy found in the area (e.g. dolomite)

enables a surface mineral map to be produced that stands in good agreement

with the known geology along the overpass. The maps generated were the

first hyperspectral overview of the surface mineralogy in this arid terrain.

Zaini (2009b) states that carbonate minerals have a more precise and

sharp vibrational absorption features at 2.30-2.35 m and 2.50-2.55 m due to

CO3-2 ion. It is the diagnostic absorption features of carbonates minerals and

the positions of absorption band are determined by the purity level and

composition of the minerals. Salisbury et al (1987) reported that the carbonate

ion have absorption bands in the wavelength range from 1500 cm-1 to 650 cm-

1 (6.67-15.38 m) due to strong fundamental molecular vibrations, a

stretching vibrational absorption around 1425 cm-1 (7.02 m) and two

bending vibrational absorptions at about 875 cm-1 and 700 cm-1 (11.43 m

and 14.28 m). Carbonate minerals have diagnostic absorption features of

reflectance spectra in the SWIR and TIR band due to electronic and

vibrational processes, as mentioned in the previous section. These spectral

features have been used to discriminate carbonate minerals from other

minerals and identify calcite and dolomite with another carbonate mineral on

the earth surface. Furthermore, the absorption features of reflectance spectra

in the visible to near infrared, which are a unique signature of each mineral,

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have also been used as an alternative technique of non-destructive testing to

analyse mineral and chemical composition of samples or rocks rapidly

(Gaffey 1986 and Van der Meer 1995).

Ground-based hyperspectral imaging combined with terrestrial lidar

scanning, a novel technique for outcrop analysis, which has been applied to

Early and Late Albian carbonates of the Pozalagua Quarry (Cantabrian

Mountains, Spain) by Kurz (2012). This study demonstrates the potential of

ground-based imaging spectroscopy to provide information about the

chemical–mineralogical distribution in outcrops, which could otherwise not

be established using conventional field methods. An image processing

workflow has been developed for differentiating limestone from dolomite,

providing additional sedimentary and diagenetic information, and the

possibility to quantitatively delineate diagenetic phases in an accurate way.

Spectral absorption signatures obtained can be linked to specific sedimentary

or diagenetic products of which some are related to iron, manganese, organic

matter, clay and/or water content. Ground-truthing of the quarry showed that

the classification based on hyperspectral image interpretation was very

accurate.

Spectral reflectance in the visible and near infrared portion of the

spectrum (0.35 to 2.55 µm) offers a rapid, inexpensive, non-destructive

technique for determining mineralogy and providing some information on the

minor element chemistry of the hard-to-discriminate carbonate minerals

(Susen 1986). It can, in one step, provide information previously obtainable

only by the combined application of two or more techniques and can provide

a useful complement to existing mineralogical and petrographic methods for

study of carbonates. According to the author Calcite, aragonite, and dolomite

all have at least 7 absorption features in the 1.60 to 2.55 pm region due to

vibrational processes of the carbonate ion. Positions and widths of these bands

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are diagnostic of mineralogy and can be used to identify these three common

minerals even when an absorption band due to small amounts of water present

in fluid inclusions masks features near 1.9 µm. Broad double bands near 1.2

µm in calcite and dolomite spectra indicate the presence of Fe2+. The shapes

and positions of these bands, if present, can aid in identification of calcites

and dolomites. Spectra may be obtained from samples in any form, including

powders, sands, and broken, sawed, or polished rock surfaces.

Hyperspectral image data sets acquired near Cuprite, Nevada, in

1995 with the SWIR Full Spectrum Imager (SFSI) and in 1996 with the

Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) are analysed with

a spectral unmixing procedure and the results compared by Neville et al

(1998). The image data, converted to radiance units, are atmospherically

corrected and converted to surface reflectances. Spectral end members are

extracted automatically from the two data sets; those representing mineral

species common to both are compared to each other and to reference spectra

obtained with a Portable Infrared Mineral Analyser (PIMA). The full sets of

end members are used in a constrained linear unmixing of the respective

hyperspectral image cubes. The resulting unmixing fraction images derived

from the AVIRIS and the SFSI data sets for the minerals alunite,

buddingtonite, and kaolinite exhibit strong similarities.

Erick and Martin (2012) present the preliminary results on the

utilisation of hyperspectral imaging for iron ore characterisation. On an iron

ore mine face, the mineralogical products derived from the hyperspectral

images such as iron oxides and kaolinite could enhance in situ grade control.

In drill chips, the high resolution images could tremendously help the

companies in measuring quickly and objectively large volume of materials

empowering the field geologists with innovative capabilities.

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Shanmugam and Abhishekh (2006) studied the potential of ASTER

image in conjunction with SRTM - DEM for bauxite exploration in the Kolli

hills of Tamilnadu state, southern India. Authors found out 16 bauxite

cappings with the help of spectral unmixing of ASTER image data and

various geological and geomorphological parameters obtained from ASTER

image. The study concludes that spectral unmixing of hyperspectral satellite

data in the VNIR and SWIR regions may be combined with the terrain

parameters to get accurate information about bauxite deposits, including their

quality.

2.10 CONCLUSION

A review of the available literatures on the importance of minerals,

mineral exploration, bauxite-iron-limestone exploration and deposits, remote

sensing and hyperspectral sensing for mineral exploration, applications of

spectral unmixing, image fusion and other image processing techniques for

mineral exploration has been elaborately discussed in this chapter.

Since not much literature exists about hyperspectral remote sensing

for bauxite, iron and limestone exploration, I though it apt to study the

potential of hyperspectral remote sensing for mapping and quality evaluation

of these three mineral resources in Tamilnadu state of south India.

Accordingly, the aim and objectives of this thesis have been formulated

(please refer Chapter 1). Though this chapter depicts only a few of the related

aspects and literatures on mineral exploration procedures, many more are

cited in Chapters 3, 4, 5, and 6 and their importance and significance are also

explained.