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Image processing and dataInterpretation
Mahesh Pal
NIT Kurukshetra
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Remote Sensing: Definition
To acquire data about an object without being incontact with it.
The art, science, and technology of obtainingreliable information about physical objects and theenvironment, through the process of recording,measuring, and interpreting imagery and digital
representations of energy pattern derived fromnon-contact sensor systems (Colwell, 1997, ASPRS )
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Remote sensing system
Different stages in remotes sensing1. Source of electromagnetic energy
2. Transmission of energy into atmosphere from source to earth.
3. Energy interaction with earth (reflection, absorption, transmission).
4. Transmission of reflected/emitted energy towards sensor.5. Detection of energy by sensor, converting it in to electrical output or
image.
6. Transmission/recording of the sensor output.
7. Pre-process of the data and production of data product.8. Collection of ground truth and other information.
9. Data analysis and interpretation.
10. Integrating interpreted images with other data for final application
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Fundamental of remote sensing by George Joseph, 2005
(source NPTEL, IIT Kanpur)
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Electromagnetic spectrum
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Name Wavelength ( m)
Optical wavelength 0.30 - 15
(a) Reflective portion 0.4 - 3.0
(i) Visible 0.4 - 0.7
(ii) Near IR 0.7 - 1.30
(iii) Middle IR 1.30 - 3.0
(b) Far IR (Thermal, Emissive) 7.00 - 15.0
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Creating Landsat Images from Raw Data: San Francisco - Oakland
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Microwave region
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Remote sensing data
Data collected by sensor onboard thespace/air/terrestrial platform is available in the form
of digital images.
Received data is processed to derive useful
information about earth features.
To interpret these images suitable corrections,
enhancements, and classification techniques are
used. A typical image interpretation may involve manual
and digital (computer assisted) procedures .
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Concept of digital Image
Digital images are array of Number and a file containingnumbers that constitute gray level values or digital number
(DN) values.
An Image is represented as a matrix of row and column.
Image is a pictorial representation of pattern of landscapewhich is composed of elements- indicators of things and
events which reflect physical, biological, and cultural
components of landscape.
Similar conditions in similar surroundings reflect similarpatterns and unlike conditions reflect unlike patterns.
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A sample image of Landsat ETM+ data
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Band 1 pixel values
Each DN in this digital
image corresponds to
one small area of the
visual image andprovide the level of
darkness or lightness of
the area.
Higher the DN value,the brighter the area.
Hence the zero value
represents a perfect
black.
The maximum value
represent perfect white
and the intermediate
values are shades of
gray.
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Landsat ETM+ image of Littleport in Cambridgeshire (England), acquired on 19
June 2000. Band combination 4,3,2
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Radar intensity image of same study area
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Digital image processing
Digital image processing involvesmanipulation and interpretation of digitalimages with the use of computer algorithms.
It is an extremely broad subject with theinvolvement of mathematical computations .
Digital image processing has many advantages
over analog image processing. It allows using a wider range of algorithms to
be applied to the input data
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Pre-processing
Operation involved in preprocessing aims to correctdegraded image acquired from sensor (raw image) to create abetter presentation of original image.
These operations are called pre-processing because theynormally precede further manipulation and analysis of image
data to extract specific information.
Radiometric correction
Atmospheric correctionGeometric correction
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Radiometric correction
Are used to modify DN values in order to
account for noise, that is, contributions to the
DN that are a function NOT of the feature
being sensed but due to:
The intervening atmosphere
The sun-sensor geometry
The sensor itself errors and gaps
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Radiometric correction
Missing lines
Striping
Illumination and view angle effects
Sensor calibration
Terrain effects
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Missing scan line
Source: earthobservatory.nasa.gov
Occurs due to error in scanning or sampling equipment of the
sensor.
Pixel values are generally missing in these lines.
Easiest method is to replace the missing value by the
corresponding pixel on the immediately preceding scan line
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Stripping or banding
transmission
striping in Landsat
2 MSS Red Green
and Blue value
Occurs due to non-identical detector responseif a detector of a electromechanical sensor goes out of adjustment and provide
reading less than or greater than other detectors fro same band over the same
ground cover.
Several methods are proposed to remove this error.
Linear method-assume a linear relation between input and outputHistogram matching- Histogram of each line is created. Stripes are
characterized by distinct histogram.
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Illumination and View angle correction
position of the sun relative to the earth changes
depending on time of the day and the day of the year.
In the northern hemisphere the solar elevation angle issmaller in winter than in summer.
An absolute correction involves dividing the DN-valuein the image data by the sine of the solar elevation
angle
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Sensor calibration
necessary to generate absolute data on physical
properties
Reflectance
Temperature
Emissivity
Backscatter Values provided by data provider / agency
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Terrain effects
Cause differential solar illumination
Some slopes receive more sunlight than others
Magnitude of reflected radiance reaching the
sensor
Topographic slope and aspect introducesRadiometric distortion
Bidirectional reflectance distribution function(BRDF)
Require DEM and sun elevation for correction
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BRDF
BRDF gives the reflectance of a target as a function ofillumination geometry and viewing geometry.
The BRDF depends on wavelength and is determinedby the structural and optical properties of the surface,such as shadow-casting, multiple scattering, mutualshadowing, transmission, reflection, absorption andemission by surface elements, facet orientationdistribution and facet density.
The BRDF is needed in remote sensing for thecorrection of view and illumination angle effects
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Radiometric correction
Atmosphere leads to selective scattering absorption
and emission. Total radiance received at sensor
depends on ground radiance (direct reflected) and
scattered radiation from the atmosphere (pathradiance).
relationship between radiance received at the sensor
(above atmosphere) and radiance leaving the ground
Ls at sensor radiance, H total downwelling radiance (incident energy),
reflectance of target (albedo), T atmospheric transmittance, Lp atmospheric path
radiance (wavelength dependent)
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In solar reflection region, scattering is mostdominant causing path radiance.
Path radiance causes (1) Reduction in contrast dueto masking effect, causing dark object appearingless dark and bright object less bright.
and (2) adjacency affectatmospheric scattering
may direct some radiation away from the sensorFOV-cause a decrease in spatial resolution ofsensor.
Two methods for correction:
Dark object subtraction and using atmospheric
models such as MODTRAN (http://modtran.org/),6S (Second Simulation of a Satellite Signal in theSolar Spectrum; http://rtcodes.ltdri.org/)
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Dark object subtraction
The most The most common atmospheric effect onremotely-sensed imagery is an increase in DN valuesdue to haze, etc.
This increase represents error and should be
removed Dark object subtraction simply involves subtracting
the minimum DN value in the image from all pixelvalues
This approach assumes that the minimum value (i.e.the darkest object in the image) should be zero
The darkest object is typically water or shadow
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Atmospheric corrections are needed in three cases:
1. When we want ratio of two bands (scattering is inverselyproportional to wavelength shorter wavelength more scattering)
2. When we want to compare upwelling radiance from asurface to some property of that surface in terms of
physically based model.
3. When comparing satellite data acquired at different dateas state of atmosphere changes from time to time.
4. Radiometric corrections for illumination, atmosphericinfluences, and sensor characteristics are done prior todistribution of data to the user.
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Atmospheric correction
Beijing, China, May 3, 2001
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Geometric correction
Geometric correction is the process of rectification ofgeometric errors introduced in the imagery during the processof its acquisition. It is the process of transformation of aremotely sensed image so that it has the scale and projectionproperties of a map.
A related technique called registration is the fitting of thecoordinate system of one image to that of a second image ofthe same area.
Geocoding and georeferencing are the often-used terms inconnection with the geometric correction process. The basicconcept behind geocoding is the transformation of satelliteimages into a standard map projection so that image featurescan be accurately located on the earth's surface, and theimage can be compared.
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Scan Skew: The main source of geometric error in
satellite data is satellite path orientation (non-polar).
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Reasons for image rectification
For scene to scene comparison of individual pixels inapplications such as change detection or thermal inertiamapping.
For GIS data for GIS modeling.
For identifying training samples according to mapcoordinates.
For creating accurate scaled photomaps.
To overlay an image with vector data such as ARC/INFO.
For extracting accurate area and distance measures.
For mosaicing. To compare images that are originally at different scales.
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The geometric correction process involves in
1. Identifying the image coordinates of several
clearly identifiable points, called ground
control points (or GCPs), in the distorted
image (A - A1 to A4).2. The true ground coordinates are obtained
from a map (B - B1 to B4, or another image ),
matching them to their true positions in
ground coordinates This is called image-to-
map or image to image registration.
To geometrically correct the original distorted image,
resampling is used to determine the DN values of new
pixel locations of the corrected image.
The resampling process calculates the new pixel values
from the original digital pixel values in the uncorrected
image.Nearest neighbour, bilinear interpolation, and cubic
convolution are three resampling methods. Nearest
neighbour method uses the DN value from the original
image which is nearest to the new pixel location in the
corrected image.
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Resampling methods
http://www.geo-informatie.nl/courses/grs20306/course/Schedule/Geometric-correction-RS-new.pdf
New DN values areassigned in 3 ways
a.Nearest Neighbour
Pixel in new grid getsthe value of closestpixel from old grid retains original DNsb. Bilinear InterpolationNew pixel gets a value
from the weightedaverage of 4 (2 x 2)nearest pixels;smoother but syntheticc. Cubic Convolution(smoothest)New pixel DNs arecomputed fromweighting 16 (4 x 4)surrounding DNs
http://www.geo-informatie.nl/courses/grs20306/course/Schedule/Geometric-correction-RS-new.pdfhttp://www.geo-informatie.nl/courses/grs20306/course/Schedule/Geometric-correction-RS-new.pdfhttp://www.geo-informatie.nl/courses/grs20306/course/Schedule/Geometric-correction-RS-new.pdfhttp://www.geo-informatie.nl/courses/grs20306/course/Schedule/Geometric-correction-RS-new.pdfhttp://www.geo-informatie.nl/courses/grs20306/course/Schedule/Geometric-correction-RS-new.pdfhttp://www.geo-informatie.nl/courses/grs20306/course/Schedule/Geometric-correction-RS-new.pdfhttp://www.geo-informatie.nl/courses/grs20306/course/Schedule/Geometric-correction-RS-new.pdfhttp://www.geo-informatie.nl/courses/grs20306/course/Schedule/Geometric-correction-RS-new.pdfhttp://www.geo-informatie.nl/courses/grs20306/course/Schedule/Geometric-correction-RS-new.pdfhttp://www.geo-informatie.nl/courses/grs20306/course/Schedule/Geometric-correction-RS-new.pdf7/27/2019 Remote sensing image processing
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Image Enhancement
Visual analysis and interpretation are oftensufficient for many purpose to extractinformation from remote sensing data.
Enhancement means altering the appearance ofdigital image so as to make it more informativefor visual interpretation.
The characteristics of each image in terms ofdistribution of pixel values will change from onearea to another.
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Image transformation and filtering are also usedas image enhancement techniques.
For visual enhancement Changing image contrastis one of the important exercise.
Contrast is defined as the difference in colourthat makes an object (or its representation in animage) distinguishable.
The range of brightness values present in animage is also referred as contrast.
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In raw imagery, the useful data often covers only a small portion of the available range
of digital values (for example for 8 bits or 256 levels). Contrast enhancement involves
changing the original values so that more of the available range is used, thereby
increasing the contrast between targets and their backgrounds.
For contrast enhancements concept of an image histogram is important.
A histogram is a graphical representation of the brightness values that comprise an
image. The brightness values (i.e. 0-255) are displayed along the x-axis of the graph. The
frequency of occurrence of each of each DN values in the image is shown on the y-axis.
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By manipulating the range of DN values represented by the
histogram of an image, various contrast enhancement techniques
can be applied.
The simplest type is a linear contrast stretch. This involves
identifying the minimum and maximum brightness values in the
image (lower and upper bounds from the histogram) and applying a
transformation to stretch this range to fill the full range.
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Histogram equalization Histogram is transformed so that
all pixels have same frequency along the whole range. This
method expands some parts of the DN range at the expenseof others by dividing the histogram into classes containing
equal numbers of pixels
Piece wise linear stretch- when histogram is bi-model. In
this approach a number of linear enhancement steps that
expands the brightness ranges by using breakpoints are
used.
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Density slicing-combining DNs of differentvalues within a specified range into a single
value. This transforms the image from a continuum of
gray tones into a limited number of gray orcolor tones reflecting the specified ranges in
digital numbers. This is useful in displayingweather satellite information.
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Filtering Filtering include a set of image processing functions
used to enhance the appearance of an image.
Filter operations can be used to sharpen or blur
images, to selectively suppress image noise, to detect
and enhance edges, or to alter the contrast of theimage.
Two broad categories
Spatial domain filtering Frequency domain filtering
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Spatial Domain Filtering
An image enhancement method that modifies pixelvalues based on the values of the surrounding pixels,
with the objective of enhancing areas of high or low
spatial frequency.
The spatial frequency of a remotely sensed image isdefined by the change in brightness value per unit
distance in any part of the image.
Rapid variations in brightness levels reflect a high
spatial frequency; 'smooth' areas with little variation
in brightness level or tone are characterized by a low
spatial frequency.
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Spatial filtering
Low pass filter -These are used to emphasizelarge homogenous areas of similar tone andreduce the smaller detail. Low frequency areasare retained in the image resulting in a smoother
appearance to the image. Average, Median andmajority filters
Original Image with a profile line Low-Pass Filtered Image and profile line
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Spatial filtering
In spatial domain filter, a moving window of a
set of pixels in dimension (i.e. 3X3 and 5X5) is
passed over each pixel in image.
Mathematical calculation using pixel value
under the window is applied and central pixel
of window is replaced by this value.
This window is called Convolution kernel.
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Convolution Filters (ERDAS)
1. Edge Detection/enhancement
2. Low pass/High Pass
3. Horizontal4. Vertical
5. Sharpen
6. Summary
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Edge detection filters highlight linear
features, such as roads or field boundaries.
These filters are useful in applications such asgeology, for the detection of linear geologic
structures (lineament).
Are used to determine boundaries ofhomogenous regions in radar images.
Roberts and Sobel filters (High pass filters)
are Mostly used.
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MedianActual
Edge detection High pass
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Frequency Domain Filter
Fourier transform of an image is expressed byamplitude spectrum which involves breaking downof an image into its frequency.
Filtering of these components is done usingfrequency domain filters.
These filters operates on amplitude spectrum of animage by removing, attenuating or amplifying theamplitudes in specific wavebands.
Frequency domain can be represented as a 2D
scatter plot known as Fourier spectrum. In whichlower frequency falls at centre and progressivelyhigher frequencies are plotted outwards.
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Fourier spectrum of ETM+ PAN image
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After applying the circular mask
Converted image
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Frequency domain filtering
1. Fourier transform of the original image to
compute Fourier spectrum.
2. Select an appropriate filter function and multiply
it by the elements of the Fourier spectrum.3. Perform an inverse Fourier transform to have an
image in spatial domain.
4. Ideal, Bartlett, Butterworth, Gaussian andHanning are some of the filters for frequency
domain filtering.
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Image Transformation
A process through which one can re-express
the information content of an Image.
image transformations generate new images
from two or more source images which
highlight particular features or properties of
interest, better than the original input images.
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Various methods
Arithmetic Operations
Principal component analysis
Hue, saturation and intensity (HSI) transformation
Fourier and wavelet transformation
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Arithmetic operations
Image addition
Image subtraction
Image division (image ratioing)
Image multiplication
These operation are done on two or more co-
registered images of same area.
Division is most widely used operation for geological,
ecological and agricultural applications.
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Vegetation indices (VIs)
Image division serves to highlight subtlevariations in the spectral responses of varioussurface covers.
By ratioing the data from two different spectral
bands, the resultant image enhances variations inthe slopes of the spectral reflectance curvesbetween the two different spectral ranges thatmay otherwise be masked by the pixel brightness
variations in each of the bands. VIs are combinations of surface reflectance at
two or more wavelengths designed to highlight aparticular property of vegetation.
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More than 150 VIs are published in scientificliterature so far.
Only a small subset have substantialbiophysical basis or have been systematicallytested.
Important VIs are:
Normalized Difference Vegetation Index (NDVI)
Simple Ratio Index (SR)
Enhanced Vegetation Index (EVI)
Atmospherically Resistant Vegetation Index(ARVI)
Soil adjusted vegetation Index (SAVI)
NDVI
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NDVI
NIR-R/NIR+R
SR
NIR/R
EVI
2.5(NIR-R)/(NIR+6R-7.5B+1)
ARVI
(NIR-(2R-B))/(NIR+(2R-B))
SAVI
(NIR-R)(1+L)/(NIR+R+L)
Where, NIR=Near Infrared, R=red, B=Blue and L=soil-
brightness dependent correction factor
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Principal component Analysis (PCA)
Image transformation techniques based onprocessing of the statistical characteristics ofmulti-band data sets to produce new bands.
Can be used to reduce data redundancy andcorrelation between bands.
The new bands are called components.
PCA attempts to maximize (statistically) the
amount of information (or variance) from theoriginal data into the least number of newcomponents.
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Actual Image
PCA1
PCA2 PCA3
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HSI transform
Images are generally displayed in RGB colours(primary colours)
An alternate to this is to hue, saturation andintensity system
Hue refers to average wavelength of the lightcontributing to the colour
Saturation specifies the purity of colour relative to
gray Intensity relates to the total brightness of a colour.
This is used for image enhancement
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HIS image of first 3 PCA file
RGB image from HSI
l f
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Image Classification
A process of assigning pixels or a group of pixels in an image
to one of a number of classes.
The output of image classification is a thematic map.
A thematic map is a map that focuses on a specific theme or
subject area (like land use/cover in remote sensing)
Land cover is the natural landscape recorded as surface
components: forest, water, wetlands, urban, etc. Land cover
can be documented by analyzing spectral signatures of
satellite and aerial imagery.
Land use is the way human uses the landscape: residential,commercial, agricultural, etc. Land use can be inferred but not
explicitly derived from satellite and aerial imagery.
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Conventional multispectral classification
techniques perform class assignments based only
on the spectral signatures of a classification unit. Contextual classification refers to the use of
spatial, temporal, and other related information,
in addition to the spectral information of a
classification unit in the classification of an image.
Object based classification (based on
segmentation techniques)
A classification unit could be a pixel, a group of
neighbouring pixels or the whole image
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Classification Approaches
There are three approaches to pixel labeling
1. Supervised
2. Unsupervised
3. Semi-supervised
Steps in supervised classification
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Steps in supervised classification
Definition of Information Classes
Training/Calibration Site Selection
Locate areas of known classes on the image
Generation of Statistical Parameters (if statistical classifier is used) define the unique spectral characteristics of selected pixels
Classification
assignment of unknown pixels to the appropriate information
class
Accuracy Assessment
test/validation data for accuracy assessment
Output Stage
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Training
pixels Test pixelsFull Image Data
Classifier Used
Principle of supervised classification
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Supervised classification
Requires training areas to be defined by the
analyst in order to determine the characteristics
of each class.
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Ground reference image of the study area
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Result of supervised classification
WaterConiferDeciduous
Legend:
Land Cover Map
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Supervised classifiers
Maximum likelihood
Neural network Decision tree
Kernel based sparse classifiers
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ijw
k
i
jk
Input
Layer
Hidden
Layer
Output
Layer
Back-propagation neural network classifier
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BY Maximum Likelihood classifier
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By Neural network classifier
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By support vector machine
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Accuracy
With ETM+ dataset using 7 classes
Classification accuracy
Classifier Accuracy (%)Maximum likelihood 82.60
Decision tree 85.60
Neural network 85.10
Support vector machine 87.37
U i d l ifi
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Unsupervised classifier
Unsupervised classification, searches for naturalgroups of pixels, called clusters, present within thedata by means of assessing the relative locations ofthe pixels in the feature space.
An algorithm is used to identify unique clusters ofpoints in feature space, which are then assumed torepresent unique land cover class.
Number of clusters (i.e. classes) are defined by user.
These are automated procedures and thereforerequire minimal user interaction.
U i d l ifi
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Unsupervised classifiers
The most popular clustering algorithms usedin remote sensing image classification are:
1. ISODATA, a statistical clustering method, and
2. the SOM (self organising feature maps), an
unsupervised neural classification method.
ISODATA
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ISODATA
Iterative Self-Organizing Data Analysis Technique Parameters you must enter include:
N - the maximum number of clusters that user
wants (depends on his knowledge about area)
T - a convergence threshold, which is the
maximum percentage of the pixels whose class
values are allowed to be unchanged between
iterations M - the maximum number of iterations to be
performed.
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ISODATA Procedure
N arbitrary cluster means are established,
The image is classified using a minimum
distance classifier
A new mean for each cluster is calculated
The image is classified again using the new
cluster means
Another new mean for each cluster is
calculated
The image is classified again.
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After each iteration, the algorithm
calculates the percentage of pixels that
remained in the same cluster betweeniterations
When this percentage exceeds T
(convergence threshold), the program stopsor
If the convergence threshold is never met,
the program will continue for M iterationsand then stop.
S i i d l ifi ti
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Semi-supervised classification
Use small number of labeled training data tolabel large amount of unlabeled data. Because collection of training data is expensive
The basic assumption of most Semi-Supervised learning algorithms Nearby points are likely to have the same label.
Similar data should have the same class label.
Two points that are connected by a path goingthrough high density regions should have the samelabel.
General image classification procedures
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1. Selecting information classes such as urban, agriculture,
forest areas, etc. Conduct field studies and collect groundinformation and other ancillary data of the study area.
2. Preprocessing of the image, including radiometric,atmospheric, geometric and topographic corrections, imageenhancement.
3. Creating a reference image from ground survey from actualimage to generate training signatures.
4. Image classification
5. Supervised mode: using training signature
6. unsupervised mode: image clustering and cluster grouping
7. Post-processing: complete geometric correction & filteringand classification decorating.
8. Accuracy assessment: compare classification results withground truth.
P t i d t i
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Parametric and non parametric
Parametric classifiers are based upon statisticalparameters (mean & standard deviation used byMLC) and based on the assumption that data arenormally distributed.
non-parametric methods make no assumptionsabout the probability distribution of the data, andare often considered robust because they may
work well for a wide variety of class distributions,as long as the class signatures are reasonablydistinct (NN, SVM, DT etc).
I /Ph t I t t ti
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Image/Photo Interpretation
An act of examining images for the purpose ofidentifying objects and judging theirsignificance.
Depending upon the instruments employedfor data collection one can interpret a varietyof images such as aerial photographs, scanner,thermal and radar imagery.
Even a digitally processed imagery requiresimage interpretation.
B i i i l f i t t ti
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Basic principal of interpretation
Image is a pictorial representation of pattern oflandscape which is composed of elements-indicators of things and events which reflectphysical, biological, and cultural components of
landscape. Similar conditions in similar surroundings reflect
similar patterns and unlike conditions reflectunlike patterns
Type and nature of extracted information dependon knowledge, skill, and experience of interpreter,method used for interpretation and understandingof its limitations.
Elements of image interpretation
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Elements of image interpretation
Shape Size
Tone
Shadow Pattern
Texture
Association Site
Time
Shape
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Shape
Shape refers to the general form or outline of an individual object.
Man made features have specific shapes
A railways is readily distinguishable from a road or a canal as its
shape consists of long straight tangents and gentle curves as
opposed to curved shape of a highway.
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Size
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Size
Length, width, height, area, volume of theobject. It is a function of image scale.
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Tone/colour
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Tone/colour Tone of an object refers to relative brightness or colour in an
image. One of the fundamental element to differentiate between
different objects.
It is qualitative measure
No feature has a constant tone.
Tone vary with the reflectivity of the object, the weather, the
angle of light on an object and moisture content of the
surface.
The sensitivity of the response of tone to all theaforementioned variables makes it a very discriminating
factor.
Slight changes in the natural landscape are more easily
comprehended because of tonal variations.
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Shadow
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Shadow
A shadow provides information about theobject's height, shape, and orientation (e.g.tree species);
Patterns
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Patterns
Similar to shape, the spatial arrangement of objects(e.g. row crops vs. pasture) is also useful to identify anobject and its usage.
Spatial phenomenon such structural pattern of anobject in an image may be characteristic of artificial aswell as natural objects such as parceling (plot of land)patterns, land use, geomorphology of tidal marshes or
shallows, land reclamation, erosion gullies, tillage,plant direction ridges of sea waves, lake districts,nature terrain etc.
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Texture
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Texture
Frequency of tonal change in particular areaof an image
A qualitative characteristics and generally
refers as rough or smooth.
Texture involves the total sum of tone, shape
pattern and size, which together give the
interpreter an intuitive feeling for thelandscape being analyzed.
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Association
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Association
Associating the presence of one object withanother, or relating it to its environment, can
help identify the object (e.g. industrial
buildings often have access to railway sidings;nuclear power plants are often located beside
large bodies of water).
For example white irregular patches adjacentto sea referees to sand.
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Site
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Site
Location of an object amidst certain terraincharacteristics shown by the image may
exclude incorrect conclusions e.g., site of an
apartment building is not acceptable in aswamp or a jungle
Time
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Time
Temporal characteristics of a series ofphotographs can be helpful in determining the
historical change of an area (e.g. looking at a
series of photos of a city taken in differentyears can help determine the growth of
suburban neighborhoods.
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Activities in image interpretation
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Activities in image interpretation
Detection
Selectively picking up the object of importance
for the particular kind of interpretation
Recognition and
identification
Classification of an object by means of specific
knowledge, within a known category, upon its
detection in the image.
AnalysisProcess of separating a set of similar objects and
involves drawing boundary lines.
Deduction
Separation of different group of objects and
deducing their significance based on converging
of evidence
Classification Establishment of the identity of objectsdelineated by analysis
IdealizationStandardization of representation of what is
actually seen in imagery.
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Applications
Assessment of Ground water Quality
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for Potability Journal of Geographic Information System, 2010, 2, 152-162
Water quality management is an important issue in the
modern times.
The data collected for Tiruchirappalli city have been utilized to
develop the approach.
Groundwater quality for potability indicated high to moderate
water pollution levels at Srirangam, Ariyamangalam, Golden
Rock and K. Abisekapurm zones of the study area, depending
on factors such as depth to groundwater, constituents ofgroundwater and vulnerability of groundwater to pollution.
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Groundwater vulnerability mapping
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Published in Applied Geography by Barnali Dixon
The overall goal of this research is to improve the methodology
for the generation of contamination potential maps by using
detailed landuse/pesticide and soil structure information inconjunction with selected parameters from the DRASTIC model.
Other objectives are to incorporate GIS, GPS, remote sensing and
the fuzzy rule-based model to generate groundwater sensitivity
maps, and to compare the results of new methodologies with the
modified DRASTIC Index (DI) and field water quality data.
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DRASTIC, an overlay and index method developed for the
Environmental Protection Agency (EPA) by the American Water
Well Association (Aller et al., 1987) is a widely used model.
This model assesses contamination potential of an area to
pollution by combining key factors influencing the solute
transport.The original DRASTIC Index (DIorg) was calculated using the most
important hydrogeologic factors that affect the potential for
groundwater pollution.
The DRASTIC Index does not provide absolute answers; it onlydifferentiates highly vulnerable areas from less vulnerable areas.
This model does not include soil structure in the model.
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The landuse data used in this study was obtained from
Landsat 5 Thematic Mapper (TM) 1992. TM images from
two seasons, spring and summer, were used in this study.
The image was classified into 4 level I classifications, suchas urban, forests, water and agriculture. Then the images
were further classified for agricultural crops.
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Satellite remote sensing of surface air quality
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In Atmospheric Environment 42 (2008) 78237843
by Randall V. Martin
Global observations are now available for a wide range of
species including aerosols, tropospheric O3, troposphericNO2, CO, HCHO, and SO2.
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HCHO (Formaldehyde) columns are closely related to surface VOC (
Volatile Organic Compounds) emissions since HCHO is a high-yield intermediate
product from the oxidation of reactive non methane VOCs.
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Hyperspectral Remote Sensing of Water
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Hyperspectral Remote Sensing of Water
Quality Parameters for Large Rivers in the
Ohio River Basin Naseer A. Shafique, Florence Fulk, Bradley C. Autrey, Joseph
Flotemersch
Compact Airborne Spectrographic Imager (CASI) datawas used.
In situ water samples were collected and a field
spectrometer was used to collect spectral data
directly from the river.
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Method of 2D and 3D Air Quality monitoring using a Lidar
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Published in 16th Conference on Air pollution Meteorology
To characterize urban and industrial pollution in FRANCE.
Lidar (Light Detection And Ranging) equipped with a scanning device, allows
realizing mapping of particles.
In industrials sites for plumes detection, urban site to show pollution from
traffic and also in a tunnel with big circulation.
During this experiment a LIDAR , works at 355 nm and have a spatial resolutionof 1.5m is used.
It is equipped with a cross- polarised channel which discriminate non-spherical
particles from the others.
For these measurements the lidar was placed at a horizontal position. The lidar
signal is inverted using the so-called slopemethod. From this calculation weretrieve the backscatter profile and then calculate an extinction value along
the optical path, and detect the plumes very accurately.
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Horizontal scanning from Lyon near a tunnel. We can observe a huge
concentration of particles at the intersection of many roads.
Mapping of heavy metal pollution in stream
sediments using combined
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g
geochemistry, field spectroscopy, and hyperspectral
remote sensing
The aim of this study is to derive parameters from spectral variationsassociated with heavy metals in soil and to explore the possibility of extending
the use of these parameters to hyperspectral images and to map the
distribution of areas affected by heavy metals on HyMAP data. Variations in
the spectral absorption features of lattice OH and oxygen on the mineral
surface due to the combination of different heavy metals were linked to actualconcentrations of heavy metals.
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Location map and HyMAP image of the Rodalquilar area, SE Spain. (a) Locations of
sampling points along the studied main stream, showing the five sections. (b) HyMAP
image acquired in 2004.
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Soil Mapping
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Soil survey and mapping using remote sensing, Tropical Ecology43(1): 61-74, 2002
M.L.MANCHANDA, M.KUDRAT & A.K.TIWARI
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Impact of industrialization on forest and non-forest
Assessing impact of industrialization in terms of LULC in a dry tropical region
(Chhattisgarh), India using remote sensing data and GIS over a period of 30 years
Environ Monit Assess (2009) 149:371376
Multi-sensor data fusion for the detection of underground coal fires, X.M. Zhang,
C.J.S. Cassells & J.L. van Genderen, Geologie en Mijnbouw77: 117127, 1999.
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http://www.fas.org/irp/imint/docs/rst/Front/tofc.html
For large number of applications of remote sensing data
www.cof.orst.edu/cof/teach/...powerpoint.../imageclassification.ppt
http://www.nrcan.gc.ca/earth-sciences/geography-boundary/remote-
sensing/fundamentals/
http://nature.berkeley.edu/~gong/textbook/
rst.gsfc.nasa.gov/
http://nptel.iitm.ac.in/courses/Webcourse-contents/IIT-
KANPUR/ModernSurveyingTech/ui/TOC1.htm
http://www.ccrs.nrcan.gc.ca/glossary/http://maic.jmu.edu/sic/rs/resolution.htm
http://www.gis.unbc.ca/courses/geog432/lectures/lect7/index.php
http://www.fas.org/irp/imint/docs/rst/Front/tofc.htmlhttp://www.cof.orst.edu/cof/teach/...powerpoint.../imageclassification.ppthttp://www.cof.orst.edu/cof/teach/...powerpoint.../imageclassification.ppthttp://www.cof.orst.edu/cof/teach/...powerpoint.../imageclassification.ppthttp://www.cof.orst.edu/cof/teach/...powerpoint.../imageclassification.ppthttp://www.cof.orst.edu/cof/teach/...powerpoint.../imageclassification.ppthttp://www.cof.orst.edu/cof/teach/...powerpoint.../imageclassification.ppthttp://www.nrcan.gc.ca/earth-sciences/geography-boundary/remote-sensing/fundamentals/http://www.nrcan.gc.ca/earth-sciences/geography-boundary/remote-sensing/fundamentals/http://nature.berkeley.edu/~gong/textbook/http://nature.berkeley.edu/~gong/textbook/http://nature.berkeley.edu/~gong/textbook/http://nature.berkeley.edu/~gong/textbook/http://nature.berkeley.edu/~gong/textbook/http://nature.berkeley.edu/~gong/textbook/http://nature.berkeley.edu/~gong/textbook/http://nature.berkeley.edu/~gong/textbook/http://www.nrcan.gc.ca/earth-sciences/geography-boundary/remote-sensing/fundamentals/http://www.nrcan.gc.ca/earth-sciences/geography-boundary/remote-sensing/fundamentals/http://www.nrcan.gc.ca/earth-sciences/geography-boundary/remote-sensing/fundamentals/http://www.nrcan.gc.ca/earth-sciences/geography-boundary/remote-sensing/fundamentals/http://www.nrcan.gc.ca/earth-sciences/geography-boundary/remote-sensing/fundamentals/http://www.nrcan.gc.ca/earth-sciences/geography-boundary/remote-sensing/fundamentals/http://www.nrcan.gc.ca/earth-sciences/geography-boundary/remote-sensing/fundamentals/http://www.nrcan.gc.ca/earth-sciences/geography-boundary/remote-sensing/fundamentals/http://www.nrcan.gc.ca/earth-sciences/geography-boundary/remote-sensing/fundamentals/http://www.nrcan.gc.ca/earth-sciences/geography-boundary/remote-sensing/fundamentals/http://www.nrcan.gc.ca/earth-sciences/geography-boundary/remote-sensing/fundamentals/http://www.nrcan.gc.ca/earth-sciences/geography-boundary/remote-sensing/fundamentals/http://www.nrcan.gc.ca/earth-sciences/geography-boundary/remote-sensing/fundamentals/http://www.nrcan.gc.ca/earth-sciences/geography-boundary/remote-sensing/fundamentals/http://www.nrcan.gc.ca/earth-sciences/geography-boundary/remote-sensing/fundamentals/http://www.cof.orst.edu/cof/teach/...powerpoint.../imageclassification.ppthttp://www.cof.orst.edu/cof/teach/...powerpoint.../imageclassification.ppthttp://www.cof.orst.edu/cof/teach/...powerpoint.../imageclassification.ppthttp://www.cof.orst.edu/cof/teach/...powerpoint.../imageclassification.ppthttp://www.cof.orst.edu/cof/teach/...powerpoint.../imageclassification.ppthttp://www.cof.orst.edu/cof/teach/...powerpoint.../imageclassification.ppthttp://www.cof.orst.edu/cof/teach/...powerpoint.../imageclassification.ppthttp://www.cof.orst.edu/cof/teach/...powerpoint.../imageclassification.ppthttp://www.cof.orst.edu/cof/teach/...powerpoint.../imageclassification.ppthttp://www.cof.orst.edu/cof/teach/...powerpoint.../imageclassification.ppthttp://www.cof.orst.edu/cof/teach/...powerpoint.../imageclassification.ppthttp://www.cof.orst.edu/cof/teach/...powerpoint.../imageclassification.ppthttp://www.cof.orst.edu/cof/teach/...powerpoint.../imageclassification.ppthttp://www.cof.orst.edu/cof/teach/...powerpoint.../imageclassification.ppthttp://www.fas.org/irp/imint/docs/rst/Front/tofc.html