Remote sensing image processing

7/27/2019 Remote sensing image processing

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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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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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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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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
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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

Remote sensing image processing

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