SBL GSS Division

Digital Image Processing of Satellite Images

ByVenugopalan Nair

Outline

1. Introduction2. Remote Sensing System3. Electro Magnetic Spectrum4. Digital Image Processing5. Radiometric corrections6. Geometric corrections7. Image enhancement8. Image classification

Self Introduction

Name: Venugopalan NairEducation: M.Sc. (Applied Geology), Barkatullah University, Bhopal, IndiaM.Tech (Remote Sensing), Bharathidasan University, Trichy, IndiaM.Tech (Hydrology), IIT, Roorkee, India

Experience: 15 Years + in GISNational Geophysical Research InstituteGB Pant Institute of Himalayan Environment and DevelopmentDefense Terrain Research LabCentral Ground Water BoardRMSISBL

Remote Sensing System

Physics of Remote Sensing

Electro Magnetic EnergyElectro magnetic radiationsElectromagnetic spectrum

Wave theory: c =

Planks theory: Q = h = hc/

Stefan Boltzmann Theory: M= /T4

Wein’s displacement law: m= A/T

Scattering: Rayleigh Scattering

Mie scatteringAdsorption: Atmospheric windows

Electro Magnetic Spectrum

Energy Interactions

Energy Interactions

Resolutions in Remote Sensing

1. Spatial Resolution

2. Spectral Resolution

3. Radiometric Resolution

4. Temporal Resolution

Spatial Resolutions

CARTOSAT IMAGESpatial Resolution: 2.5m

LISS IV ImageSpatial Resolution 5.8m

Land sat Image Spatial Resolution 30m

Spectral Resolution

Characteristics of commonly used bands

Radiometric Resolutions

Temporal Resolution

Sample Satellite Image

Satellite Image Procurement

IMAGE SPECIFI CATION

IMAGE SEARCH

IMAGE FINALIZATION IMAGE ACCEPTANCE

RESULTVERIFI CATION

IMAGE PROCUREMENT

NOYES

IMAGE ORDER

PROCURED IMAGE(Input I mage)

CRITERIA:Cloud coverVintageArea coverage

1. Sun Angle

2. Nadir angle

3. STD/Ortho ready

Digital Image Processing

PIXEL

Structure of a digital Image

Structure of a digital Image

BSQ (Band Sequential Format): Each line of the data followed immediately by the next line in the same

spectral band.

BIP (Band Interleaved by Pixel Format):The first pixel for all bands in sequential order, followed by the second

pixel for all bands, followed by the third pixel for all bands, etc., interleaved up to the number of pixels.

BIL (Band Interleaved by Line Format):The first line of the first band followed by the first line of the second

band, followed by the first line of the third band, interleaved up to the number of bands. Subsequent lines for each band are interleaved in similar fashion.

Formats of a digital Image

120 150 100 120 103

176 166 155 85 150

85 80 70 77 135

103 90 70 120 133

20 50 50 90 90

76 66 55 45 120

80 80 60 70 150

100 93 97 101 105

210 250 250 190 245

156 166 155 415 220

180 180 160 170 200

200 0 123 222 215

Band 2 Band 3 Band 4

10 15 17 20 21

15 16 18 21 23

17 18 20 22 22

18 20 22 24 25

20 50 50 90 90

76 66 55 45 120

80 80 60 70 150

100 93 97 101 105

120 150 100 120 103

176 166 155 85 150

85 80 70 77 135

103 90 70 120 133

210 250 250 190 245

156 166 155 415 220

180 180 160 170 200

200 0 123 222 215

BIL

10 15 17 20 21

20 50 50 90 90

120 150 100 120 103

210 250 250 190 245

15 16 18 21 23

76 66 55 45 120

176 166 155 85 150

156 166 155 415 220

17 18 20 22 22

80 80 60 70 150

85 80 70 77 135

180 180 160 170 200

18 20 22 24 25

100 93 97 101 105

103 90 70 120 133

200 0 123 222 215

BSQ

10 20 120 210 15

15 76 176 156 16

17 80 85 180 18

18 100 103 200 20

50 150 250 17 50

66 166 166 18 55

80 80 180 20 60

93 90 0 22 97

100 250 20 90 120

155 155 21 45 85

70 160 22 70 77

70 123 24 101 120

190 21 90 103 245

415 23 120 150 220

170 22 150 135 200

222 25 105 133 215

BIP

Color composites of Images

Digital Image Processing

1. Image rectification and restoration

2. Image enhancement

3. Image classification

Geometric correction

Causes of distortionPanoramic distortionsEarths curvature

Geometric correction

Geo Referencing

Using Feature matching

Using DGPS points

Using reference coordinates/grid

Radiometric corrections

• Sun elevation correction• Earth sun distance correction• Haze compensation correction• DN to absolute radiance conversion• Noise removal

•Stripping or banding•Line drops•Bit errors or spiky image

Image Enhancement

Grey level thresholding

Level Slicing

Contrast stretching

DN’ = ((DN-MIN)/(MAX-MIN))*255

Histogram equalization

Spatial Filtering

Hi pass filtersLow Pass filters

Spatial Filtering

Spatial Filtering

Pan Sharpening/Resolution Merging

Mosaicking

Colour balancing

Tiling

Digital Elevation Models

Stereo I mage Procurement

Image download into compatible format

Epipolar image Generation

QC/ QA

Final DEM

YES

NO

DGPS Points

Acquisition of GCP in Stereo Modeling

Acquisition of Tie Points in the Stereo Modeling

Computed Output DEM

Manual Editing of DEM to cover the failure Areas

Reference DGPs

DTM MapContour Map

Digital Elevation Models

Ortho rectification

Classification

1. Supervised classification

2. Unsupervised classification

3. Hybrid classification

1. Spectral pattern recognition

2. Spatial Pattern recognition

3. Temporal pattern recognition

Supervised Classification

1. Training site identification

2. Spectral signature collection

3. Statistical analysis

4. Classification Methods

5. Process running

Supervised Classification

Supervised Classification

• Advantages– Analyst has control over the selected classes

tailored to the purpose– Has specific classes of known identity– Does not have to match spectral categories on the

final map with informational categories of interest– Can detect serious errors in classification if

training areas are misclassified

Supervised Classification

• Disadvantages– Analyst imposes a classification (may not be

natural)– Training data are usually tied to informational

categories and not spectral properties• Remember diversity

– Training data selected may not be representative– Selection of training data may be time consuming

and expensive– May not be able to recognize special or unique

categories because they are not known or small

Unsupervised Classification

1. Algorithm based

2. Inbuilt methods

Unsupervised Classification

• Advantages– Requires no prior knowledge of the region– Human error is minimized– Unique classes are recognized as distinct units

• Disadvantages– Classes do not necessarily match informational

categories of interest– Limited control of classes and identities– Spectral properties of classes can change with time

Unsupervised Classification

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