4.ijaest vol no 6 issue no 2 comparision of quac and flaash atmospheric correction modules on eo 1...

7/27/2019 4.IJAEST Vol No 6 Issue No 2 Comparision of QUAC and FLAASH Atmospheric Correction Modules on EO 1 Hyperion

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Dataset Attribute Sanchi dataset

Entity ID EO1H1450442011037110KZ

Acquisition Date 6th Feb 2011

NW Corner 23.484927N, 77.687076E

NE Corner 23.470050N, 77.758742E

SW Corner 22.637785N, 77.482487E

SE Corner 22.622987N, 77.553704E

Subset NW Corner 23.536851N, 77.699425E

Subset NE Corner 23.535278N, 77.773400E

Subset SW Corner 23.407691N, 77.667750E

Subset SE Corner 23.406135N, 77.741948E

Ref. Datum WGS84

Map Projection UTM

Zone No. 43

Image Cloud Cover 0 to 9% Cloud Cover

Receiving Station SGS

Scene Start Time 2011 037 05:03:25

Scene Stop Time 2011 037 05:07:44

Date Entered Feb 6, 2011

Target Path 145

Target Row 044

Orbit Path 145

Sun Azimuth 140.634988

Sun Elevation 41.672499

Orbit Row 44

Sensor Look Angle 4.9418Browse Available Y

Comparision of QUAC and FLAASH Atmospheric Correction Modules on EO-1 HyperionData of Sanchi

Gaurav Agrawal

Research Scholar

MANIT Bhopal

Prof. Dr. Jyoti Sarup

Associate Professor

MANIT Bhopal

[email protected]

Abstract:

The process, which transforms the data fromspectral radiance to spectral reflectance, is knownas atmospheric correction, compensation, orremoval. Hyperion images are the rich source ofinformation contained in hundreds of narrowcontiguous spectral bands. There are number ofatmospheric agents which contaminate the content

of various bands information. To get the completeadvantage of Hyperion data it is required to applyatmospheric correction so that the influence ofatmosphere on the Earth observation data can beremoved. Primary type include scene based andradiation transmission model based algorithms.Major scene based algorithms are IAR and ELMand latest is QUAC. MODTRAN is very popularand effective atmospheric transmission model forcorrecting mutispectral and hyperspectral data.MODTRAN based FLAASH algorithm available inENVI is very effective for Hyperion dataatmospheric correction. In this paper QUAC and

FLAASH available in ENVI has been applied foratmosphere correction of Hyperion data andcomparative analysis is carried out.

Key Words : IAR- Internal Average Reflectance,ELM- Empirical Line Method, MODTRAN-

MODerate resolution atmospheric TRANsmission,FLAASH- Fast Line-of-sight Atmospheric Analysis

of Spectral Hypercube, QUAC QUick AtmosphericCorrection, SAM- Spectral Angle Mapper

I- Introduction

Hyperion sensor is a hyperspectral imager on-boardof Earth Observation -1 (EO-1) satellite. Theprocess, which transforms the data from spectralradiance to spectral reflectance, is known asatmospheric correction, compensation, or removal.

A. Study area-The study area is situated 40 kms from Bhopal innorth east direction known as Sanchi, in Vidisha

district of Madhya Pradesh state. This place is

famous for ancient Buddhist Stoops a worldmonument and Protected by ArcheologicalDepartment of India as a Place of Heritage. The

area of study is mainly constituted by sandstones,Basalts and black cotton soil. The major landforms

are hills, pediments and pediplane of sandstonesand basalts. The major river is Betwa flowing

towards south . The Agricultural fields are mainly

the wheat. The climatic conditions are moderatewith average rainfall between 900-1000mmannually.

B. Data sets and methodologyThe DataSet properties and the image of study areais as shown in following figure and table. In our

data set there were only 158 bands were calibratedand less noisy.

C. Software used-ENVI 4.7, ERDAS 9.2

Gaurav Agrawal* / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES

Vol No. 4, Issue No. 1, 178 - 186

ISSN: 2230-7818 @ 2011 http://www.ijaest.iserp.org. All rights Reserved. Page 178


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Fig. 1 FCC of Sanchi Area Fig-2 Hyperions Swath Width and Length(USGS, 2004 )

II- Hyperspectral Data- Hyperion

The data of EO-1 are archived and distributed by theUSGS Center for Earth Resources Observation andScience (EROS) and placed in the public domain.There are 242 spectral bands ranging from 356 to2577 nm. Out of which only 198 bands arecalibrated and hence can be used for furtherprocessing. The spatial resolution of Hyperion is 30

meter. Each Hyperion scene is collected as a narrow

strip, covering a ground area approximately 7.7 kmin the across-track direction, and 42 km or 185 kmin the along-track direction (depending on the

original data acquisition request). The product isdistributed by USGS, and the level one product,which is only radiometrically corrected, is available

(Pearlman, et al., 2003; USGS, 2004a).

A. Concepts of Atmospheric Corr ectionRadiation entering a sensor is classified as in Fig. 3.Atmospheric correction is the processing to eliminateS2, S3 and clouds and Gaseous absorption which arecontaminating the observed pixels.

B. Need of Atmospheric Correction for EO-1Hyper ion I mages

EO-1 Hyperion hyperspectral images are the richsource of information contained in hundreds ofnarrow contiguous spectral bands. There are number

of atmospheric agents which contaminate the contentof various bands information. To get the completeadvantage of Hyperion data it is required to applyatmospheric correction so that some bands whichcontain useful information and contaminated byatmospheric agents that can be retrieved. Theatmospheric correction is often considered as acritical pre-processing step to achieve full spectralinformation from every pixel especially withhyperspectral data. In hyperspectral image analysissome approaches has been implemented usingspectral library or field spactra. If atmosphericcorrection is not applied then there is markedlydifference between observed spectral radiance andspectral library or field spectra. These differencesmay negatively influence the accuracy to which theimage analysis has been carried out based on anindependent spectral library or field spectra (PerryE.M et al., 2000).


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Fig. 3: Radiation Entering a Sensor

Where, S1: Radiance to be observed,S2: Radiance from atmospheric dispersion (Neighboring effect)

S3: Path Radiance

III- Atmospheric Correction Approaches

Atmospheric correction may be applied bycollecting information from scene (image) or bymodelling radiation transmission throughatmosphere.

A- Scene Based Empir ical ApproachesThese approaches are based on the radiancevalues at present in the image (i.e. scene)therefore they are known as scene basedempirical approaches. IAR and ELMapproaches are commonly used by variousresearchers. QUAC is an Advance developmentwhich is analysed here.

A.1 I nternal Average Relative (IAR)

ReflectanceThe Internal Average Relative (IAR)

Reflectance approach (Kruse, F.A., 1988)calculates the average spectrum of a scene. Thespectrum of any pixel in the scene is thendivided by the average spectrum to estimate therelative reflectance spectrum for the pixel. Thisapproach does not need any field measurementsof reflectance spectra of surface targets. Thisapproach is mostly applicable for imaging dataacquired over arid areas without vegetation.

A.2 QUAC

QUAC is a visible-near infrared throughshortwave infrared (VNIR-SWIR) atmospheric

correction method for multispectral andhyperspectral imagery. Unlike other firstprinciples atmospheric correction methods,it determines atmospheric compensation

parameters directly from the information

contained within the scene (observed pixelspectra), without ancillary information. QUACis based on the empirical finding that theaverage reflectance of a collection of diverse

material spectra, such as the endmember

spectra in a scene, is essentially scene-independent. All of this means significantlyfaster computational speed compared to the

first-principles methods.QUAC also allows forany view or solar elevation angle. Should a

sensor not have proper radiometric orwavelength calibration, or the solar illuminationintensity be unknown (such as when a clouddeck is present), this approach still allows the

retrieval of reasonably accurate reflectancespectra.

QUAC provides the following:

Automated atmospheric correction of MSI

and HSI data in the solar reflective spectralregion (~0.4-2.5 m).

Support for AISA, ASAS, AVIRIS, CAP

ARCHER, COMPASS, HYCAS, HYDICE,HyMap, Hyperion, IKONOS, Landsat TM,

LASH, MASTER, MODIS, MTI, QuickBird,RGB, and unknown sensor types.

QUAC creates an image of retrieved surfacereflectance, scaled into two-byte signedintegers using a reflectance scale factor of10,000.


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B. Radiation Tr ansport Models based approachThe scene based approaches like IARR andELM are not generally producing very goodresults, as the linearity assumption, whichpresumes uniform atmospheric transmission,

scattering and adjacency effects throughout thescene, may not be accurate. In radiation transportmodeling efforts are made to understand andremove the effects of major atmosphericprocesses with radiation such as absorption andscattering. Very effective and latest atmospheric

transmission model is MODerate resolutionatmospheric TRANsmission (MODTRAN).

MODTRAN

is an algorithm and computer model, which is

developed by the Air Force Research Laboratory

(AFRL) in collaboration with Spectral Sciences,

Inc . (SSI). MODTRAN calculates atmospherictransmittance and radiance for frequencies from0 to 50,000 cm- 1 at moderate spectral resolutionof 1 cm- 1. MODTRANs internal minimumspectral resolution of 1 cm-1 which correspondsto a spectral resolution of about 0.625 nm at theuppermost wavelength (Ientilucci et al., 2008).The latest model is MODTRAN 4 which is thenewly released radiative transfer model whichprovides accuracy required for the processing ofhyperspectral imagery. Therefore FLAASH aMODTRAN 4 based approach is discussed infurther sections.

B.1 Fast L ine -of-Sight Atmospher ic Analysis of

Spectral Hypercubes (FLAASH)

FLAASH is a first-principles atmosphericcorrection tool that corrects wavelengths in thevisible through near- infrared and shortwaveinfrared regions, up to 3 m. Unlike many other

atmospheric correction programs that interpolateradiation transfer properties from a pre-calculated

database of modelling results, FLAASHincorporates the MODTRAN4 radiation transfer

code. You can choose any of the standardMODTRAN model atmospheres and aerosol types

to represent the scene; a unique MODTRANsolution is computed for each image.

FLAASH also includes the following features:

Correction for the adjacency effect (pixelmixing due to scattering of surface-reflected

radiance)

An option to compute a scene-average visibility(aerosol/haze amount). FLAASH uses the most

advanced techniques for handling particularlystressing atmospheric conditions, such as thepresence of clouds.

Cirrus and opaque cloud classification map

Adjustable spectral polishing

FLAASH supports hyperspectral sensors (such as

HyMAP, AVIRIS, HYDICE, HYPERION, Probe-1, CASI, and AISA) and multispectral sensors

(such as ASTER, IRS, Landsat, RapidEye, andSPOT). Water vapor and aerosol retrieval are only

possible when the image contains bands inappropriate wavelength positions . In addition,

FLAASH can correct images collected in eithervertical (nadir) or slant-viewing geometries.

IV- Implementation of Atmospheric Correction

AlgorithmsHyperion data is corrected using various

algorithms available in ENVI software.

A. QUAC ModelThe QUAC data processing scheme is outlinedbelow

Fig 4 Quac Processing

QUAC performs a fast and fairly accurateatmospheric correction with the followingconditions:

There are at least 10 diverse materials in ascene.

There are sufficiently dark pixels in a scene to

allow for a good estimation of the baselinespectrum.


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B. FLAASH ModelThis section is a brief overview of the

atmospheric correction method used byFLAASH.FLAASH starts from a standard equation for

spectral radiance at a sensor pixel, L, thatapplies to the solar wavelength range (thermal

emission is neglected) and flat, Lambertianmaterials or their equivalents. The equation is asfollows:

(

1)where:

is the pixel surface reflectance

e is an average surface reflectance for the pixel

and a surrounding region

S is the spherical albedo of the atmosphereLa is the radiance back scattered by the

atmosphereA and B are coefficients that depend onatmospheric and geometric conditions but not on

the surface.

Each of these variables depends on the spectralchannel; the wavelength index has been omittedfor simplicity. The first term in Equation (1)corresponds to radiance that is reflected fromthe surface and travels directly into the sensor,while the second term corresponds to radiancefrom the surface that is scattered by theatmosphere into the sensor. The distinctionbetween and e accounts for the adjacency

effect (spatial mixing of radiance among nearbypixels) caused by atmospheric scattering. To

ignore the adjacency effect correction, set e =

. However, this correction can result insignificant reflectance errors at shortwavelengths, especially under hazy conditionsand when strong contrasts occur among thematerials in the scene.

The values of A, B, S and La are determinedfrom MODTRAN4 calculations that use theviewing and solar angles and the mean surface

elevation of the measurement, and they assumea certain model atmosphere, aerosol type, andvisible range.

V - Analysis

A. Spectral Resul t of Atmospheric cor rection-The Atmospheric Correction Module Changes the

Reflectance to Radiance and radiance values are changed

in to reflectance values. The Application at a point inStudy area shows is like this

Fig 5- Comparison of radiance to reflectance of plot of

same pixel in 3 images a) QUAC corrected b)

Original(not corrected) c) FLAASH corrected

B. Spectra Comparison of Main Classes-B.1 Vegetation Spectra-

As per Fig -6 Reflectance Properties of Vegetationin the VNIR and SWIR part of the spectrum are

dominated by the strong atmospheric absorption

regions and absorption properties of the chlorophylla and b pigments. Narrow absorption at 760 nmcorresponding to O2 is compensated by QUAC aswell as FLAASH. Pigments in Vegetation show

absorption at 640 and 660 nm. Spurious peaks inboth the spectra at 940 nm indicated the strong

water absorption is under estimated by both models.


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Fig 6 Vegetation Spectra Fig 8 Rock body Spectra

B.2 Water Body Spectra-

Water bodies have a different response to EMR thanwater bounded up in molecules in that they do not exhibit

discrete absorption features. Water has a hightransmittance for all visible wavelengths, but the

transmittance increases with decreasing wavelength.However , Suspended materials and pigments cause

increased reflectance in visible region(Van Der Meer andDe Jong , 2003). In the near infra red and in SWIR allEMR is absorbed by water. FLAASH and ATCOR

corrected spectra exhibits spurious spikes in 1900 to 2500nm wavelengths indication over estimation of watervapour absorption . Similar observations have beenreported in SWIR region (Kruse,2003)

Fig -7 Water Body Spectra

B.3 Rock Sample Spectra-Quartzites of Sanchi primarily comprises of Quartz,

Feldspar, Haematite, Muscovite and other traceminerals like Nontronite, Pyrope, Montmorillonite and

minerals of Aluminium and Iron with Sulphur . TheSanchi Quartzites sample was taken for this analysis

which contained within one pixel. It is hard and

compact exhibiting concoidal fractures when brokenwith a rock hammer. A megascopic examination of therock sample shows quartz as the major mineral in thesample in association with minerals like Nontroniteand Pyrope. Quartz does not exhibit any significant

absorption feature and is considered as featurelessspectrum, while minerals such as Montmorillonite andtrace mineral Nontronite exhibit absorption feature at1420 nm and 1915 nm. This absorption feature is seen

in the QUAC and FLAASH extracted spectra at thesame wavelength. The presence of the absorption

feature is checked by Spectral Analyst tool and further

presence of this absorption feature is confirmed by theSpectrometer field spectra of quartzite taken from thestudy area, and is seen in Figure-8

V-Result and Discussion-

A. Matching parametersThe spectral angle mapper (SAM) has been widelyused as a spectral similarity measure. It calculatesspectral similarity between the reference reflectancespectrum (usgs spectral library spectrum) and the test

spectrum (image spectrum). The angle between twospectra is used as a measure of discrimination. Theresult of SAM is an angular difference measured inradian ranging from zero to/2 which gives aqualitative estimate of similarity between imagespectrum and spectrometer spectrum (Van der Meerand De Jong, 2003). Small spectral angle valuescorrespond to high similarity between image spectra


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Data and Study Area-

and spectrometer spectra. Larger angle valuescorrespond to less similarity.

Sample no(pixelvalues)

SAMQUAC

SAMFLAASH

1.Rock(751,302) 0.0322 0.03292.Vegetation(882,266) 0.0966 0.03123.Water(696,352) 0.3753 0.5175

4.Rock(827,393) 0.2656 0.14815.Rock(823,607) 0.0504 0.0436Average .1640 .1541

B. Spectra Matching Resul ts-SAM values from different pixel of study area dataset

shows that the Sam values of different samples forQUCA and FLAASH are similar average values are

lesser for the FLAASH which gives a better result

than QUAC.

C. Attr ibute Compar ison-Attribute comparison is as per table.

VI- ConclusionAs the SAM comparison shows that there is not a bigdifference in Spectra matching values for QUACandFLAASH but FLAASH has an uper hand and givebetter comparison results but it requires fullknowledge of the area , time of flight , sensorelevation etc . QUAC performs a good approximateatmospheric correction to FLAASH or other physics-based first-principles methods, generally producingreflectance spectra within approximately +/-15% ofthe physics-based approaches so QUAC can be usedfor atmospheric corrections of the Hyperspectralimages of unknown areas. The future modification andimplementation of hyperspectral image and QUACatmospheric corrected image is in analysis of surfaceof other planet images as most of them have noatmosphere . The Hyperspectral technique has a goodscope of research for extra terrestrial studies.

VII References

Pearlman, J.S., Barry, P.S., Segal, C.C., Shepanski, J.,Beiso, D., Carman, S.L., (2003). Hyperion, a SpaceBorne Imaging Spectrometer, IEEE Transactions on

Geosciences and Remote Sensing, vol.41, no.6,pp.1160-1173.

Perry E.M., Warner T. and Foote P., (2000),Comparison of atmospheric modeling versus empiricalline fitting for mosaicking HYDICE imagery,International Journal of Remote Sensing, vol: 21, no. 4,pp.799-803.

Adler-Golden S., Berk, A, , Bernstein, L.S.,Richtsmeier, S., Acharya, P.K., and Matthew, M.W.,Aderson, G.P, Allred, C. L., Jeong, L.S., and Chetwynd,J.H.,(2008), FLAASH, A MODTRAN4 AtmosphericCorrection Package for Hyperspectral Data Retrievaland Simulations.ftp://popo.jpl.nasa.gov/pub/docs/workshops/98_docs/2.pdf

Ientilucci E. J., (2008), Using MODTRAN PredictingSensor-Reaching Radiance, Chester F. Carlson Center

for Imaging Science, Rochester Institute ofTechnology,www.cis.rit.edu/~ejipci/Reports/Modtran_lab.pdf

Kruse, F. A., (1988), Use of airborne imagingspectrometer data to map minerals associated withhydrothermally altered rocks in the northern GrapevineMountains, Nevada and California, Remote Sensing ofEnvironment,vol.24, pp.31-51.

Kruse F. A., (2008), Comparison of ATREM,ACORN, And FLAASH Atmospheric Corrections

using low altitude AVIRIS data of Boulder, Co,USA,http://www.hgimaging.com/FAK_Pubs.htm.

USGS, 2004a. Earth Observing 1, downloaded on May,2009, from, url:

http://eo1.usgs.gov/

Comparison of Attribute Required

Parameters FLAASH QUAC

Sensor type Hyperion Hyperion

Data Type BIL, BIP BIL, BIP,BSQ

Pixel size 30 -

Ground

elevation0.6 km -

Scene centre

Lat/Long

23.05 N,

77.62 E-

Visibility 40 km -

Sensor altitude 703.3166

km-

Flight date &

flight time

06/02/2011

05:05:17-

Atmospheric

model

Tropical -

Aerosol model Rural -

Watervapour

1135 nm -

Spectral

polishingNo -

Wavelength

calibration

Yes -

Advanced

parameters-


Vol No. 4, Issue No. 1, 178 - 186

http://www.cis.rit.edu/~ejipci/Reports/Modtran_lab.pdfhttp://www.hgimaging.com/FAK_Pubs.htmhttp://www.hgimaging.com/FAK_Pubs.htmhttp://www.hgimaging.com/FAK_Pubs.htmhttp://eo1.usgs.gov/http://eo1.usgs.gov/http://eo1.usgs.gov/http://www.hgimaging.com/FAK_Pubs.htmhttp://www.cis.rit.edu/~ejipci/Reports/Modtran_lab.pdf


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USGS, 2004b, EO-1, User Guide version 2.3,downloaded on May, 2009, from,

eo1.usgs.gov/documents/EO1userguidev2pt320030715UC.pdf

Van der Meer, F., 2004. Analysis of spectralabsorption features in hyperspectral imagery.

International Journal of Applied Earth Observation and

Geoinformation, 5(1): 55-68.

Van der Meer, F. and De Jong, S., 2003. ImagingSpectrometery. Basic Principles and ProspectiveApplications, 4. Kluwer Achademic Publishers,Dordrecht/ Boston/ London, 35 pp.


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4.ijaest vol no 6 issue no 2 comparision of quac and flaash atmospheric correction modules on eo 1...

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