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Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
1st semester AND GEOINFORMATICS
Lecture Notes of Prof. Dr. M. Hahn
Last printed 20 September 2011 Page 1 of 56
Module 2
Photogrammetry and
Remote Sensing
Lecture Notes
Prof. Dr. M. Hahn
WS 2011/2012
Topic: Remote Sensing (Part 1)
Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
1st semester AND GEOINFORMATICS
Lecture Notes of Prof. Dr. M. Hahn
Last printed 20 September 2011 Page 2 of 56
Remote Sensing (Part 1)
Table of Contents
1. Basic Principles of Remote Sensing
1.1. Definitions, overall Remote Sensing process 1.2. Electromagnetic radiation 1.3. The electromagnetic spectrum 1.4. Interaction of electromagnetic radiation with the atmosphere 1.5. Interaction of electromagnetic radiation with Earth-surface
material 1.6. Energy sources and sensing 1.7. Satellite images and visualization
2. Preprocessing of remotely-sensed data
2.1. Removal of data errors 2.2. Registration and geometric correction 2.3. Atmospheric correction 2.4. Sensor calibration
3. Classification
3.1. Concept of supervised and unsupervised classification 3.2. Scatterplot and decision making 3.3. Supervised classification 3.4. Unsupervised classification
Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
1st semester AND GEOINFORMATICS
Lecture Notes of Prof. Dr. M. Hahn
Last printed 20 September 2011 Page 3 of 56
Chapter 1
Basic principles of Remote Sensing
1.1 Definitions, Overall Remote Sensing process
‘Remote Sensing is the science (and to some extent: art) of acquiring
information about the Earth’s surface without actually being in contract
with it. This is done by sensing and recording reflected or emitted energy
and processing, analysing and applying that information.’
Source: Canada Centre for Remote Sensing, CCRS Tutorial
‘Remote Sensing: the science and art of obtaining useful information
about an object, area, or phenomenon through the analysis of data
acquired by a device that is not in contact with the object, area or
phenomenon under investigation.’
T.M. Lillesand and R.W. Kiefer
Remote Sensing and Image Interpretation, Wiley book
‘Remote Sensing may be broadly defined as the collection of the
information of natural sources and environmental information about an
object without being in physical contact with the object. The term Remote
Sensing is restricted to methods that employ electromagnetic energy as
the means of detecting and measuring target characteristics.’
F.F. Sabins,
Remote Sensing principles and interpretation, Freeman book
Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
1st semester AND GEOINFORMATICS
Lecture Notes of Prof. Dr. M. Hahn
Last printed 20 September 2011 Page 4 of 56
‘The science of Remote Sensing consists of the interpretation of
measurements of electromagnetic energy reflected or emitted by a target
from a vantage-point that is distant from the target.’
‘Earth observation (EO) by Remote Sensing is the interpretation and
understanding of measurements …… ’
P.M. Mather
Computer processing of Remotely-sensed images, Wiley book
‘Aircraft and satellites are the common platform from which Remote
Sensing observations are made.’
F.F. Sabins
Remote Sensing principle and interpretation, Freeman book
Elements of an overall Remote Sensing process
Energy Source
The first requirement for remote sensing is to have an energy source
which illuminates or provides electromagnetic energy to the target of
interest.
Radiation and the Atmosphere
As the energy travels from its source to the target, it will interact with the
atmosphere it passes through.
Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
1st semester AND GEOINFORMATICS
Lecture Notes of Prof. Dr. M. Hahn
Last printed 20 September 2011 Page 5 of 56
This interaction may take place a second time as the energy travels from
the target to the sensor.
Source: CCRS Tutorial
Interaction with the Target
The energy (electromagnetic radiation) interacts with the target depending
on the properties of both the target and the radiation.
Recording of Energy by the Sensor
After the energy has been scattered by, or emitted from the target, a
sensor is required to collect and record the electromagnetic radiation.
Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
1st semester AND GEOINFORMATICS
Lecture Notes of Prof. Dr. M. Hahn
Last printed 20 September 2011 Page 6 of 56
Transmission, Reception, and Processing
The energy recorded by the sensor has to be transmitted, often in
electronic form, to a receiving and processing station where the data are
processed into an image (hardcopy and/or digital).
Interpretation and Analysis
The processed image is interpreted, visually and/or digitally (image
analysis), to extract information about the target which was illuminated.
Application
The extracted information assists to solve a particular problem.
1.2 Electromagnetic radiation
Electromagnetic energy /radiation is the means by which information is
transmitted from an object (target) to a sensor.
1.2.1 Basic terminology
Energy - the capacity to do work, expressed in J
(Joules)
Radiant energy - the energy associated with electromagnetic
radiation (EMR)
Flux of energy - the rate of transfer of energy from one
Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
1st semester AND GEOINFORMATICS
Lecture Notes of Prof. Dr. M. Hahn
Last printed 20 September 2011 Page 7 of 56
place to another (Latin word, meaning =
‘flow’)
- is measured in W (Watts)
Radiant flux density - to understand the interaction between
electromagnetic radiation and surfaces
Radiant flux is the rate of transfer of radiant
(=electromagnetic) energy
Density implies variability over the two-dimensional
surface on which the radiant energy falls.
Radiant flux density is the magnitude of the radiant flux
that is incident upon or, conversely, is emitted by a surface of
unit area (measured in W/m2)
== Irradiance (if radiant energy falls upon a surface)
== Radiant emittance ( if the energy flow is away from surface)
Radiance - is the radiant flux density transmitted from a unit
area on the Earth’s surface as viewed through a unit solid (3D) angle.
is measured in steradians
= 3 D equiv. of the radian
Example thermal energy solar energy reflected
emitted by the Earth by the Earth
Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
1st semester AND GEOINFORMATICS
Lecture Notes of Prof. Dr. M. Hahn
Last printed 20 September 2011 Page 8 of 56
Assume:
Diffuse reflectance = radiance incident
upon the surface is back scattered in all
upward directions
Then: (based on diffuse reflection)
A proportion of the radiant flux might be measured per unit solid viewing
angle
--- this proportion is the radiance
Radiance is measured in watts per square meter per
steradiant [ W/(m2*sr) ]
surface
flux
solid (3D ) angle
surface
normal
source area
Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
1st semester AND GEOINFORMATICS
Lecture Notes of Prof. Dr. M. Hahn
Last printed 20 September 2011 Page 9 of 56
Reflectance --- is the ratio between the irradiance and the
radiant emittance of an object.
Diffuse reflectance ( see above)
Specular reflectance:
= angle of incidence and angle of reflectance
are equal and no scattering occurs at the surface.
Remarks:
1 When remotely-sensed images collected over a time period
(= multi-temporal images) are to be compared it is common
practice to convert the radiance values recorded by the sensor
into reflectance factors in order to eliminate the effects of
variable irradiance over the seasons of the year.
2 All these described quantities refer to particular wavebands
rather than to the whole electromagnetic spectrum.
Precede the terms by the adjective spectral
Spectral reflectance, spectral irradiance,……etc.
angle of
reflection
angle of
incidence
Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
1st semester AND GEOINFORMATICS
Lecture Notes of Prof. Dr. M. Hahn
Last printed 20 September 2011 Page 10 of 56
1.2.2 Nature of electromagnetic radiation (or the view of quantum
mechanics)
Controversy in physics over the past 250 years
EMR: wave theory corpuscular theory
considers radiation as considers radiation as
a wave form a stream of particles
(wave-like form of energy) so-called photons
Importance to remote sensing
Today’s view of quantum mechanics: EMR is both a wave and a stream
of particles.
1.2.3 Wave characteristics of electromagnetic radiation
EMR is travelling at a velocity c (=speed of light) equal to 3*108 m/s in a
sinusoidal, harmonic fashion.
EMR consists of an electrical (E) field and a magnetic (M) field
the wave-like characteristics of
EMR allows the distinction with
regard to wavelength e.g.
microwave, infrared radiation
in order to understand the
interactions between EMR
and the Earth’s atmosphere
and surface.
Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
1st semester AND GEOINFORMATICS
Lecture Notes of Prof. Dr. M. Hahn
Last printed 20 September 2011 Page 11 of 56
Source: CCRS tutorial
Characterisation of electromagnetic waves
Wavelength ---- length of one wave cycle
λ = distance between successive wave crests
Frequency ---- number of cycles (or crests) of a wave passing a fixed f point per unit of time ( = 1 sec)
Wavelength and frequency are related according to
c= λ * f
c --- speed of light (is essentially constant) = 3*108m/s (in a vacuum)
λ --- unit is [m] or mm, m, m
f --- unit is [Hz=cycle/sec] inverse is period T = 1/f
= time elapsed in seconds per cycle
Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
1st semester AND GEOINFORMATICS
Lecture Notes of Prof. Dr. M. Hahn
Last printed 20 September 2011 Page 12 of 56
Examples:
Source: CCRS tutorial
Explanation
i) 2.5 cycles per second f = 2.5Hz
period = 0.4 sec per cycle
ii) 4 Hz
iii) 1.5 Hz
Example 1: Given f = 4 Hz, calculate λ:
with c = λ*f = 3*108 m /sec = 300 000 km/sec
and f = 4 Hz
λ = c/f = (3*108m/sec)*(1/4)sec = 75 000 km
Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
1st semester AND GEOINFORMATICS
Lecture Notes of Prof. Dr. M. Hahn
Last printed 20 September 2011 Page 13 of 56
Example 2: Given wavelength in micrometers, λ = 1µm, calculate f:
λ = 1µm (Near infrared)
= 10-6m
f = (3*108m/sec) / (1/10-6m) = 3*1014 Hz
These examples demand for a closer look to the EM spectrum.
1.2.4 Corpuscular characteristics of electromagnetic radiation
In the particle description, electromagnetic energy travels in quanta
(discrete units) of energy.
The energy of a quantum is given as
Q = h * f
Q = energy of a quantum (Joules J)
h = Planck’s constant h=6.26*10-34 J*sec
f = frequency (Hz=1/sec)
Energy Q is delivered to a target.
Note: delivery is on a probabilistic basis - not in such a
way that it is evenly spread over the wave
Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
1st semester AND GEOINFORMATICS
Lecture Notes of Prof. Dr. M. Hahn
Last printed 20 September 2011 Page 14 of 56
Relate the wave model to the quantum model
Substituting f = c/λ into Q = h*f
yields Q = h*c/λ h*c = constant
Conclusion:
The shorter the wavelength , the higher the energy content
and vice visa shorter wavelengths are easier to sense.
Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
1st semester AND GEOINFORMATICS
Lecture Notes of Prof. Dr. M. Hahn
Last printed 20 September 2011 Page 15 of 56
The Electromagnetic Spectrum
The electromagnetic spectrum represents the continuum of
electromagnetic energy
from extremely short wavelengths (cosmic and gamma rays)
to extremely long wavelengths (radio and television waves)
Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
1st semester AND GEOINFORMATICS
Lecture Notes of Prof. Dr. M. Hahn
Last printed 20 September 2011 Page 16 of 56
The names assigned to regions of the spectrum make a discussion of the
spectrum more convenient. In each of the regions adjacent
wavelengths ‘’behave similarly’’ or are generated by similar
mechanisms.
However the division between UV and visible or microwave and
thermal infrared is not hard. The regions blur into each other.
Three regions are of particular importance for RS:
a) The visible spectrum (visible light)
is so called because it is detected by the eyes, whereas other forms of
EMR are invisible to the unaided eye.
The spectrum range of visible light is 0.4-0.7 um
wavebands are perceived as particular colours:
waveband
violet -- blue -- green -- yellow - orange -- red
0.40 0.46 0.50 0.58 0.60 0.62 0.70 µm
Blue, green and red are the primary colours or wavelengths of the
visible spectrum. All other colours can be formed by combining
R-G-B.
Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
1st semester AND GEOINFORMATICS
Lecture Notes of Prof. Dr. M. Hahn
Last printed 20 September 2011 Page 17 of 56
b) The infrared spectrum (infrared = beyond the red)
covers the wavelength range from approximately 0.7 µm to 100 µm
Regions: Near IR Mid IR Thermal/Far IR
Note: different definitions/boundaries are found in the literature.
c) The microwave spectrum
ranges from submillimetre to 1 (to 3) metres
further subdivided in bands : K, X, C, S, L, P – band
Some microwave sensors can detect small amounts of radiation at
those wavelengths that are emitted by the Earth. passive sensors
But the important RS microwave sensors are all active systems
Generation, transmission and recording of the reflected radiation
0.7 100 1.3 3.0 µm
Radiation property
reflective emissive, radiative, thermal
reflected energy
≈ visible light
emitted from Earth’s surface in the form of heat
Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
1st semester AND GEOINFORMATICS
Lecture Notes of Prof. Dr. M. Hahn
Last printed 20 September 2011 Page 18 of 56
Question: which spectral bands can be used most effectively in RS?
Figure: Wavelengths that can be used most effectively
depends on interaction with the Earth’s atmosphere
(particles and gases in the atmosphere)
obviously absorption (cf. figure above) happens not everywhere and
not to the same degree in the spectrum
high spectral transmission in the visible area and other
“atmospheric windows”
energy level of the sun has its peak in the visible area
all passive RS sensor systems have to take these two aspects
(transmission and energy) into account.
the heat energy emitted by the Earth corresponds to a windows around
10 µm (max energy) in the thermal IR
Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
1st semester AND GEOINFORMATICS
Lecture Notes of Prof. Dr. M. Hahn
Last printed 20 September 2011 Page 19 of 56
1.4 Interaction of Electromagnetic Radiation with the
Atmosphere
As the energy travels from a source (the sun) to the target (the Earth’s
surface) it interacts on its travel with the Earth’s atmosphere.
Interaction with particles and gas molecules in the
atmosphere
The total amount of radiation that strikes an object is equal to
reflected off absorbed by transmitted through
the object the object the object
Two mechanisms of interaction:
scattering absorption
reflected
radiation
absorbed
radiation
transmitted
radiation
incident
radiation + + =
Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
1st semester AND GEOINFORMATICS
Lecture Notes of Prof. Dr. M. Hahn
Last printed 20 September 2011 Page 20 of 56
1.4.1 Scattering
Particles or large gas molecules present in the atmosphere cause
the EMR to be redirected from its original path.
How much scattering takes place depends on
the wavelength of the radiation
the abundance of the particles or gases
the distance the radiation travels through the atmosphere
Three types of scattering take place:
a) Rayleigh scattering
occurs when particles are very small (0.1 µm and less)
compared to the wavelength of the EMR
particles: small specks of dust or nitrogen and oxygen
molecules
shorter wavelengths of energy are much more scattered than
longer wavelengths
Rayleigh scattering is the dominant scattering
mechanism in the upper atmosphere
Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
1st semester AND GEOINFORMATICS
Lecture Notes of Prof. Dr. M. Hahn
Last printed 20 September 2011 Page 21 of 56
Remarks:
Blue sky phenomenon (during the day) ---- stronger scattering of the
blue wavelength of the sunlight
the blue light seems to reach our eyes from all directions
Sunrise and sunset ---- the scattering of the shorter
wavelengths is more complete (longer distance through
atmosphere)
longer wavelengths (orange, red) penetrate
b) Mie scattering
occurs when particles are just above the same size
as the wavelength of the radiation
particles: dust, pollen, smoke (industrial or domestic pollution),
water vapour, salt particles from oceanic evaporation
Mie scattering tends to affect longer wavelengths than those
affected by Rayleigh scattering.
Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
1st semester AND GEOINFORMATICS
Lecture Notes of Prof. Dr. M. Hahn
Last printed 20 September 2011 Page 22 of 56
occurs mostly in the lower portions of the atmosphere
where larger particles are more abundant.
dominates when cloud conditions are overcast.
(a) and (b) are selective scattering processes i.e. scattering affects
specific wavelengths of energy.
c) Nonselective scattering
occurs when the particles are much larger than the wavelength
of the radiation (above 10 µm)
particles: water droplets, ice fragments, large dust particles
all wavelengths are scattered about equally
causes fog and clouds to appear white to our eyes.
All wavelengths are scattered (by the water droplets) in
approximately equal quantities. (∑ white light)
Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
1st semester AND GEOINFORMATICS
Lecture Notes of Prof. Dr. M. Hahn
Last printed 20 September 2011 Page 23 of 56
1.4.2 Absorption
Molecules present in the atmosphere absorb energy at various
wavelengths.
The three main constituents (gases) which absorb radiation are
ozone
carbon dioxide
water vapour
Ozone absorbs the harmful UV radiation from the sun.
protective layer in the atmosphere avoids skin burn
Carbon dioxide — the “greenhouse gas”
tends to absorb radiation in the far infrared portion of
the spectrum thermal heating
and serves to trap this heat inside the atmosphere.
Water vapour absorbs much of the longwave IR and shortwave
microwave radiation. The presence of water vapour in the lower
atmosphere greatly varies from location to location and at different times
of the year.
(Little water vapour above deserts but high humidity in the tropics)
Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
1st semester AND GEOINFORMATICS
Lecture Notes of Prof. Dr. M. Hahn
Last printed 20 September 2011 Page 24 of 56
These gases absorb EM energy in very specific regions of the spectrum
(called absorption bands ). Those areas which are not severely
influenced by absorption are called atmospheric windows. .
(= areas which are useful for RS purposes).
cf. figure in section 1.3 transmission
Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
1st semester AND GEOINFORMATICS
Lecture Notes of Prof. Dr. M. Hahn
Last printed 20 September 2011 Page 25 of 56
1.5 Interaction of EMR with Earth surface materials
Electromagnetic energy that is not absorbed or scattered in the
atmosphere can reach and interact with the Earth’s surface.
Three forms of interaction:
Absorption
Transmission
Reflection
The total incident energy will interact with the target in one or more of
these three ways.
The proportions of each will depend on
The wavelength of the EMR
The material and condition of the target
Target dependency: There will be a variation of the interaction from time
to time during the year.
Example: vegetation, from leafing stage to maturity.
Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
1st semester AND GEOINFORMATICS
Lecture Notes of Prof. Dr. M. Hahn
Last printed 20 September 2011 Page 26 of 56
A: absorption: radiation is absorbed into the target
T: transmission: radiation passes through a target
R: reflection: radiation “bounces” of the target, is redirected
reflected energy travels upwards through the atmosphere
(interaction with the atmosphere)
that part which enters the field of view of the sensor is
detected and recorded by the sensor
most interest in RS is in measuring radiation reflected from
targets
Amount and distribution of reflected energy are used in RS to infer
the nature of the reflecting surface.
Background: basic assumption made in remote sensing is that specific
targets (soils, rocks, vegetation, water, ) have an
individual and characteristic manner of interacting with
incident radiation.
Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
1st semester AND GEOINFORMATICS
Lecture Notes of Prof. Dr. M. Hahn
Last printed 20 September 2011 Page 27 of 56
spectral response
Distinction between two types of reflection that occur at a surface:
specular reflection diffuse reflection
mirror-like
directed away in a
single direction
(no scattering)
αi = αr
reflected almost
uniformly in all
directions (scattered in
all directions)
like a piece of paper
- specular and diffuse reflection represent the two extreme ends of the
way in which energy is reflected
- most Earth surface features are located somewhere between
perfectly diffuse or perfectly specular
αi
incidence
angle
reflection
angle αr
Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
1st semester AND GEOINFORMATICS
Lecture Notes of Prof. Dr. M. Hahn
Last printed 20 September 2011 Page 28 of 56
- but: in the visible part of the spectrum
terrestrial targets ≈ diffuse reflectors
calm water ≈ specular reflector
diffuse reflector -- rough surface
specular reflector -- smooth surface
Rough/smooth is defined by surface variations or particle sizes
that make up the surface in comparison to the wavelength of the incoming
radiation
Example: fine-grained sand
would appear fairly smooth to microwaves (long
wavelength) but quite rough to the visible (short wavelength).
Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
1st semester AND GEOINFORMATICS
Lecture Notes of Prof. Dr. M. Hahn
Last printed 20 September 2011 Page 29 of 56
Examples: target interactions --- leaves/vegetation and water
--- visible and infrared wavelength
Spectral response curve / pattern
(sometimes also called the spectral signature )
A spectral reflection curve describes the spectral response of a target for
a certain region e.g. 0.4 - 2.5 µm.
Note: A satellite sensor operating in the visible and NIR region does not
observe and detect all reflected energy FOV.
To make use of such measurements, the distribution of radiance
all possible observation and illumination angles, called the
bi-directional reflectance distribution function (or BRDF) must be
taken into consideration
water (1)
water (2)
vegetation 20
10
30
0.5 0.7 0.6 0.4 0.8 λ (µm)
reflectance
%
Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
1st semester AND GEOINFORMATICS
Lecture Notes of Prof. Dr. M. Hahn
Last printed 20 September 2011 Page 30 of 56
Spectral response curses for about 2000 materials can be found in the
JPL ASTER library:
e.g. 9 different ones for water/ snow/ ice
1350 minerals
etc.……
Reminder
Example
1. leaves: lower reflection = higher absorption in B, R
higher reflection in G
very higher reflection in IR ---- not plotted
2. water: lower reflection = higher absorption in R, NiR
darker if viewed in R, NiR
higher reflection in B, G
water looks blue, green
violet blue green yellow orange red
0.4 0.46 0.50 0.58 0.60 0.62 0.70
Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
1st semester AND GEOINFORMATICS
Lecture Notes of Prof. Dr. M. Hahn
Last printed 20 September 2011 Page 31 of 56
Example
Target interaction with leaves
Summer : chlorophyll content is at its maximum “greenest”
Autumn : less chlorophyll
healthy leaves: internal structure of leaves act as all excellent diffuse
reflector of NiR wavelengths
extremely bright ( but not visible to our eyes )
measure + monitor the near-iR reflectance to determine healthiness
of vegetation
Target interaction with water
longer wavelength radiation R, NiR is absorbed more by water
than shorter wavelength
water looks blue or blue-green due to stronger reflectance
of B,G and darker if viewed in R, NiR
Chlorophyll absorbs radiation
in B, R wavelengths but
reflects G
Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
1st semester AND GEOINFORMATICS
Lecture Notes of Prof. Dr. M. Hahn
Last printed 20 September 2011 Page 32 of 56
If sediment is present in the upper layers of the water body.
slight shift to longer wavelengths ( green, yellow ) because of a
better reflectivity brighter appearance of water.
Chlorophyll (algae) absorbs blue, reflects green
water will appear in a more green colour
Topography of the water surface (rough, smooth, floating material,
etc.) can also lead to complications for water-related interpretations
due to problems of specular reflection or other influences on colour or
brightness.
spectral response can be quite variable, even for the same target
type, and can also vary with time and location.
important to know where to “look” spectrally
Water and vegetation is similar in the visible area but
completely different in NiR
Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
1st semester AND GEOINFORMATICS
Lecture Notes of Prof. Dr. M. Hahn
Last printed 20 September 2011 Page 33 of 56
1.6 Energy Sources and Sensing
The sun is the most obvious source of the electromagnetic energy
measured in remote sensing. The sun’s energy is either reflected
(visible, reflected iR) or absorbed and re-emitted (thermal iR).
EM energy that is naturally available comes from a passive source.
The RS instruments which detect the naturally available energy are called
passive sensors.
Passive source:
solar energy
Visible
iR (include thermal)
UV, X- and Gamma-ray
Passive sensors: Can only be used to detect energy when natural energy is available. reflected energy: -- requires illumination of the Earth (daytime)
re-emitted energy: -- can be detected day or night, as long as the amount of energy is large enough.
Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
1st semester AND GEOINFORMATICS
Lecture Notes of Prof. Dr. M. Hahn
Last printed 20 September 2011 Page 34 of 56
Sensing
Recording with passive sensors: Reflected energy is mainly recorded by
instruments which travel in sun-synchronous orbits (the satellite
travels southwards over the illuminated side of the Earth and crosses the
Equator at the same local Sun time on each orbit). Data are recorded only
on the way from North pole to South pole because the other
half of the orbit is in the Earth shadow.
Active sensors provide their own energy source for illumination.
The sensor emits radiation which is directed toward the target.
The reflected radiation is detected and recorded by the sensor.
Active sensors can be used to examine wavelengths that are not
sufficiently provided by the Sun, such as microwaves.
Microwave imaging radar (synthetic aperture radar, SAR) and laser
scanner (airborne platform) are examples of active sensors.
Man-made energy source
An active system requires the
generation of a fairly large amount
of energy to adequately illuminate
targets.
Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
1st semester AND GEOINFORMATICS
Lecture Notes of Prof. Dr. M. Hahn
Last printed 20 September 2011 Page 35 of 56
1.7 Satellite Images and Colour Display
Satellite-borne sensors record digital images in channels or bands
which represent reflected radiation of specific wavebands.
Example:
SPOT 1, 2
(2 HRV-instruments)
SPOT 4
1986, 1990 1998
XS-bands 0.50 – 0.59 um 1.58 –1.75 um
(mid iR
additionally)
0.61 – 0.68 um
0.79 – 0.89 um
M (= pan) band 0.51 – 0.73um
Note: SPOT3, launched in 1993, failed.
Data formats for digital satellite imagery
Unfortunately, no world-wide standard for storage and transfer
of remotely-sensed data has been agreed upon specific
procedures for reading satellite image data requested
CEOS (Committee on Earth Observation Satellites) tries to
standardise.
Display of satellite images
Display of one band: grey scale image
Display of three bands: pseudo colour image
Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
1st semester AND GEOINFORMATICS
Lecture Notes of Prof. Dr. M. Hahn
Last printed 20 September 2011 Page 36 of 56
Example: (Honolulu data)
6 bands, wavelength between 0.4 and 12 µm
Note: different waveband width of the individual bands.
Inspection of three images of Honolulu (cf. the corresponding pictures)
Band 2 0.45 – 0.52 um (blue-green 0.46–0.5–0.58 µm)
Band 7 0.76 – 0.90 um (NiR 0.7 – 1.0/1.3 µm)
Band 11 0.5 – 14.0 um (thermal iR 3 – 15 µm)
Observation: For different regions of Honolulu different brightness levels
can be observed in different wavebands
value of obtaining multiple images at different wavelength.
Discussion:
1) Region indicated by (a) in band 2
blue wavelength: one can see through the shallow water
along the coast line.
2) Region (b) in band 2 and band 7
blue: rain forest appears fairly dark
NiR: rain forest (vegetation) appears quite bright.
reflective nature of chlorophyll.
Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
1st semester AND GEOINFORMATICS
Lecture Notes of Prof. Dr. M. Hahn
Last printed 20 September 2011 Page 37 of 56
3) Region (c) in band 11
Thermal iR: dark patches are clouds, which in the thermal iR are
cold.
Blue, NiR: at shorter wavelengths the high reflectivity of the water
droplets leads to bright patches.
4) Region (d) in band 11
Bright areas in the thermal band that are dark in the other bands.
Areas (d) include parts of the airport runways which are facing the
sun are warmer than the average scene and so are bright.
Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
1st semester AND GEOINFORMATICS
Lecture Notes of Prof. Dr. M. Hahn
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Chapter 2
Preprocessing of the Remotely-sensed Data
The raw remotely-sensed image data generally contain flaws or
deficiencies. Removal of flaws and correction of deficiencies are termed
preprocessing. Some corrections are carried out at the ground
receiving station.. Nevertheless there is often a need on the user’s
part for some further preprocessing.
Preprocessing may include:
Corrections for geometric, radiometric and atmospheric deficiencies
Removal of flaws (data errors)
Note: not all operations will be applied in all cases.
2.1 Removal of data error
Defects in the data can be due to errors in the scanning or sampling
equipment or in the transmission or recording of image
data.
a) Partially or entirely missing scan lines
are normally seen as horizontal black (0) and white (255) lines on the
images.
Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
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b) Horizontal banding pattern
electro-mechanical scanners (Landsat’s MSS and TM) have several (a
small number of) detectors that are used in the scanning process. The
imbalance in the six (MSS) detectors shows up by strips (banding
pattern) in the image.
Missing scan lines and banding patterns can be considered to be a
cosmetic defect that interferes with the visual appreciation of the patterns
on the image. It might be even more problematic for statistical/pattern
analysis of images.
a) Missing scan lines
There is no means of knowing which values should be present at missing
scan lines.
Solution: Estimate the values by looking at the data values of
the scan lines above and below.
Background:
Spatial autocorrelation = points that are close geographically tend to
have similar values.
Therefore, neighboring pixels of objects will strongly correlate
Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
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Processing for replacement of missing scan lines
Option 1: Replace a missing pixel value by the value of the
corresponding pixel on the immediately preceding scan line.
Option 2: Replace the missing value by the average of the
neighbouring pixels on the scan lines above and below of a defective line.
I (i, k) = (I (i, k-1) + I (i, k+1))/2
Note: read as ''pixel i on scan line k''
Option 3: Use the neighboring bands of multi-spectral imagery.
For instance, the Landsat (1 to 3) MSS band 4 (green) and 5 (red) are
normally highly correlated. In general, bands in the same region of the
spectrum are highly correlated and can be used to correct missing scan
lines.
I ( i, k, b) = σb/σr*( I( i, k, r) - ( I(i, k+1, r) + I(i, k-1, r) )/2 )
+ ( I(i, k+1, b)+ I(i, k-1, b) )/2
b, r -- bands
Detection of missing scan lines
is a tedious task if such lines are located interactively (by visual
examination)
k
band b band r
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the auto-correlation property can be used for semi-automatic
localization. E.g. by comparison of the average grey values of
neighbouring scan lines. In case of large differences search along
these scan lines for unexpected sequences of values (strings of either
0's or 255's). Mark the suspected sequence and display it for inspection
by a operator.
b) De-striping methods
A horizontal banding pattern is sometimes seen on Landsat’s MSS and
TM data. (electro-mechanical scanners). This pattern is more apparent
when seen against a dark, low-radiance background such as water
areas. The MSS has six detectors for each band (MSS: 4 spectral
bands) why the banding pattern is known as sixth-line banding in Landsat
MSS images. TM has 16 detectors per band and produces seven bands
of imagery.
The underlying idea of de-striping is based upon the assumption that
each detector “sees” a similar distribution of all the land cover
categories that are present in the imaged area. In consequence,
the histograms generated for a given band from the pixel values
produced by all n detectors should be identical. This implies that
the mean and standard deviation of the data from each detector should
be the same. To get rid of the stripping effects the means and standard
deviations are calculated from lines 1, 7, 13, 19, …… (histogram 1),
Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
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lines 2, 8, 14, 20, ……(histogram 2) and so on (MSS six detector
situation).
All n histograms are equalized by forcing mean and standard deviation
to be equal to the corresponding average values of mean and standard
deviation of all of the pixels in the image.
2.2 Registration and geometric correction
Registration is the fitting of the coordinate system of one image
to that of a second image (or map) of the same area.
Geometric correction or rectification is a related technique. An image is
transformed so that it has the scale and projection properties of a
map.
The integration of information extracted from remotely-sensed images
with map data into a GIS requires registration. Image
registration, also called rubber-sheeting, is typically defined by a
polynomial transformation of an image to a set of control points.
For presentation of RS images in a map-like form rectification (geometric
correction) has to be carried out. Rectified images can be overlaid with
maps or used to locate features of interest on the map and the image.
Rectification may also be used to bring adjacent images into
registration or to overlay images of the same area acquired by
different sensors. Rectification procedures of photogrammetry range from
simple plane rectification to the more complex process of generating
digital orthophotos.
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Sources of geometric errors in digital satellite imagery are
instrument errors
- distortions of the optical system, non-linearity of the scanning
mechanism and non-uniform sampling rates.
panoramic distortion
is a function of the angular field of view and affects instruments with wide
AFOV (such as AVHRR) more than those with a narrow AFOV (Landsat
MSS + TM, SPOT HRV)
Earth rotation
During the movement of a satellite southwards above the earth’s surface
the Earth moves eastwards thus the effect of Earth rotation is to skew the
image.
Skew angle at latitude L:
θ = 900- arccos (sinθEquator / cos L )
Satellite’s
ground track
scan lines at time t1
Potential scan lines at time t2
without Earth rotation
scan lines at time t2 with Earth rotation
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platform instabilities
Include variations in altitude and attitude . The information needed
to correct for the variations is not generally available (modern
satellites carry GPS, INS, star sensors, ……) or not precise enough
for correction of the image data. Therefore a correction band on
nominal orbital parameters must be replaced by a transformation using
ground control points.
Instead of the attempt to define the sources of error and their effects an
alternative method is to look at the problem from the opposite end ,
the differences between the positions of points recorded on image and
map can be used to estimate the distortions present in the image.
Processing:
a) Relate the image and map coordinate system by an empirical
transformation.
--- commonly polynomials of second or third order are used for
map-to-image (image-to-map) coordinate transformation.
b) Locate suitable ground control points by using GPS or locate gcp’s
on the map and measure its corresponding image coordinates.
Note: gcp chips (19*19 pixels) of existing image maps may also be used.
c) Estimate the transformation parameter by least squares
and
d) Determine the pixel values of the rectified image by resampling
(gray scale interpolation)
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Third-order polynomial for mapping (x, y) map coordinates to (r, c) image
coordinates (and vice versa).
X = a00+a10*c+a01*r+a20*c2+a11*c*r+a02*r
2+a30*c3+a21*c
2*r+a12*c*r2+a03*r3
Y = b00+b10*c+b01*r+b20*c2+b11*c*r+b02*r
2+b30*c3+b21*c
2*r+b12*c*r2+b03*r3
The unknowns are the parameters aij, bij of the transformation.
First order polynomial: 6 unknowns ( 3 or more gcp’s)
Second order polynomial: 12 unknowns ( 6 or more gcp’s)
Third order polynomial: 20 unknowns ( 10 or more gcp’s)
To have reasonable redundancy significantly more gcp’s should be used.
Experience:
Around (<) 10 gcp’s give acceptable result for a first-order fit with a
small image area of up to 10242 pixels.
More gcp’s will be needed in area of moderate relief (where a second-
order polynomial may be required)
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2.3 Atmospheric correction
From Chapter 1 it is already known that a value recorded at a given pixel
location is not a recording of the true ground-leaving radiance at
that point. Scattering redirects some of the incoming EM energy and
some of the reflected EM energy within the atmosphere into the field of
view of the sensor.
Relationship for estimating atmospheric effects on multi-spectral images
in the 0.4 – 2.4 µm reflective solar region:
LS = Htotal * ρ * T + LP (2.1)
Htotal is the total downwelling radiance in a specific spectral band
sensor
ground
Module 2, Remote Sensing 1 MSc Course PHOTOGRAMMETRY
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ρ is the reflectance of the target.
(ratio: “downwelling”/“upwelling” or irradiance/radiant emittance)
T is the transmittance given by the transmission curves as a
function of the wavelength.
LP is the atmospheric path radiance.
The reflectance ρ relates to the interaction of EMR to the target,
the transmittance to the interaction with the atmosphere.
Model (2.1) is a simplified model which does not explicitly take account of
the following aspects:
reflectance of a surface will vary with the view angle as well as with the
solar illumination angle (particularly important for wide FOV and off-
nadir viewing)
the slope of the ground and the disposition of topographic features.
More complex models are developed but operational use of these models
is limited by the need to supply data relating to the condition of
the atmosphere at the time of imaging. The costs of such data-collection
activities is considerable, hence reliance is placed upon the use of
“standard atmospheres” such as “mid-latitude summer”. In this case
only a very small number of parameters, e.g. the horizontal visibility
in kilometres have to be supplied.
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Atmospheric correction might be beneficial in three cases:
1) If ratios of the values in two bands of a multi-spectral image are
computed
e.g. the normalised difference vegetation index.
NDVi=(NiR-R)/(NiR+R)
study vegetation patterns
A simple technique for compensation of atmosphere path radiance
might be sufficient.
2) If upwelling radiance from a surface is related to some property of
that surface in terms of a physically based model
=> the atmospheric component must be estimated and removed.
3) If results found at one time are to be compared with results achieved
at a later date
=> the state of the atmosphere will undoubtedly vary from time 1 to
time 2.
R
NiR
Estimate of path radiance
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2.4 Sensor calibration
Sensor calibration, combined with atmospheric and view angle correction,
aims at the estimation of target reflectance. A number of
methodologies for the calibration of the Landsat TM optical bands, SPOT
HRV, other optical sensors and Radar are proposed.
The relationship between radiance and pixel value (PV) can be defined
for spectral band as
Ln* = a0 + a1 *PV
Where a0 and a1 are offset and gain coefficients and Ln* is apparent
radiance at the sensor. (Measured in units of mW/(m*sr*µm))
Spot provides gain values ai in the header of the XS image. The
apparent radiance of a given pixel is calculated from
L = PV/ ai
Given the value of radiance L it is usual to convert to apparent reflectance
by
ρ= (π*L*d2)/(ES*cos(θS))
d = relative Earth - Sun distance
Es = exoatmospheric solar irradiance
θs = solar zenith angle (Reference: Floyd F. Sabins, Remote
Sensing: Principles and Interpretation, 3rd edition, W.H.Freeman and company, 1997
Note: ρ is not corrected
for atmospheric effects
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Chapter 3 Classification
3.1 Supervised and unsupervised classification
Image classification can be decided into two categories:
Supervised and unsupervised classification.
Supervised classification refers to the process of measuring
characteristic features of the entities (or objects) one wants to classify
by using training sets of known objects or object classes
and use them to determine the class membership of all other pixels in an
image.
Unsupervised classification is a clustering process which aims
at the determination of the number of distinct, naturally occurring
groups and the allocation of pixels to these groups (or classes). In this
respect it can be considered as a segmentation technique which
aims at subdividing an image into meaningful regions.
In both cases the properties (features) of the pixel to be classified are
used to label that pixel. In the simplest case, a pixel is
characterised by a vector whose elements are its grey levels
in each spectral band. This feature vector (also called pattern) represents
the spectral properties of that pixel. Further features such as
texture or context may be included in the feature vector.
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Note: If classification does not lead to proper results:
i. try to find a more sophisticated classification scheme not
recommended
ii. try to find better or more features, e.g. from other sensors (Laser,
Radar, in addition to optical sensors) or existing databases (DTMs,...)
3.2 Scatterplot (scattergram) and decision making
A scatterplot is one of the easiest ways to perceive the distribution of
values measured on two features. One feature is plotted against the
other for each pixel and the vector (feature 1, feature 2) determines the
position of that pixel in the two-dimensional Euclidean space.
Example
Pixel Feature 1 / R Feature 2 / NiR
Water body low low
Vigorous vegetation
Low high
Feature 2
NiR, 255
Feature 1
R, 255
0
0
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The position of a point in the feature space is directly
related to the values of the two features. Obviously points belonging to
the same class tend to cluster and points belonging to different classes
tend to be separated. This is the underlying assumption
of any classification scheme.
Example 2
R NiR MidiR
Water body low low low
Shadow region low low Any grey value possible
Adding a third feature leads to a three-dimensional scattergram. The
problem of N-dimensional feature spaces is that they can not be
visualised properly.
3D 1+2 3 two-dimensional scatterplots
4D 1+2+3 6 two-dimensional scatterplots
5D 1+2+3+4 10 two-dimensional scatterplots
Decision making:
Given a scatterplot (cf. figure 3.2 ) one can recognise
Two district clusters
The compactness of each cluster
The distance in feature space (example: d12, d23 )
A linear decision boundary (boundary between two clusters/classes)
use several 2D
scatterplots ?
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Figure 3.2 Decision making
3.3 Supervised classification
Supervised classification methods require external knowledge of
the area shown in the image. This knowledge may be derived from
fieldwork, analysis of aerial photos, maps or other sources like reports.
Most statistical methods assume that the type of the distribution of
features in each class is known and only parameters derived from sample
data ( training samples ) have to be estimated before using them to
make classification decisions. (Parametric decision making or parametric
classification methods.)
Training samples (learning phase)
Task: Determine statistical characteristics of each class (the number of
classes must be known)
If X is a feature vector: X =
Feature 2
Feature 1
②
③
①
feature value 1 feature value 2 feature value n
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Determine:
mean X
extreme values: min, max, for each feature in each class
variances and covariances –> variance–covariance matrix
Supervised classification methods
1) Parallelepiped or box classifier
all points within min-max region (box)
class i
all other points are unclassified
2) K-means or centroid method
calculate mean/centre X of each training class
calculate Euclidean distance from each unknown pixel
to the centre of each class. The pixel is given the label of the
centre to which its distance is smallest. (nearest centre decision)
2X
4X
3X
1X3X
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In n-dimensional feature space the decision boundary corresponds to a
hyper plane which is perpendicular to the line connecting the two
centroids 1X and 2X .
3. Maximum likelihood method
If one of two neighbouring clusters is much smaller than the other one, it
makes sense to move the boundary between them closer to the
centroid of the smaller one. Similarly, if the clusters are elongated in a
certain direction, the boundary should be tilted toward the direction of
their elongation.
model of probability distribution (Gaussian normal distribution)
variance
covariance
matrix
Probability that P belongs to 1X is higher than probability that
P belongs to 2X .
2X P
equi-probability
contours
1X
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3.4 Unsupervised classification
Occasionally points will form such distinct clusters that automated
means of discovering which points belong together with be successful.
This process is referred to unsupervised learning.
Principles of automatic cluster formation
1. Each point is considered a separate embryonic cluster. In an iterative
process points (clusters) are merged together if they are closer
than any other two points. The iteration stops either when the
expected number of clusters has been found or when the next points
to be added to a clusters is more than some threshold distance away.
K-means clustering
2. Initially the whole collection of points is considered to be one huge
cluster. Iteratively, existing clusters are split along lines of
weakness in two clusters. The splitting is repeated until some limits
(max number of expected clusters) are passed. Splitting can be
combined with merging to improve the results.
ISODATA algorithm
(Iterative Self-Organising Data Analysis Technique)
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