Geospatial Image Processing Services
Transcript of Geospatial Image Processing Services
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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