The document provides information on polarimetric SAR data analysis techniques. It discusses decompositions like m-delta, m-chi and m-alpha. It also covers topics like speckle filtering, color composition, scattering mechanism estimation, classification using Wishart and SVM classifiers, and comparison of polarimetric decomposition and classification results from RISAT-1 and other SAR sensors. Workflows shown include preprocessing, parameter estimation, decomposition, filtering, geocoding, co-registration of datasets and multi-sensor classification and analysis. Case studies over Mumbai and Saoner are presented to demonstrate the techniques.

1. Shaunak De
Centre of Studies In Resources Engineering – IIT Bombay
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5. • Input Product Directory
• Sub-Directories and Files will be detected
Automatically
• Output Directory
• Exported products will be placed here
Multilook Factor and other
information displayed here
Leader files, Scenes, Data Files, Grid
Files, Meta Files are listed here
• Enter the desired Range multilook
• Azimuth multilook is automatically
calculated
Choose the desired
outputs and click “Run”
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6. • Two channel data – i.e. RH and RV
• Supplied as I,Q (complex) 16 bit
integer values
• After conversion to float C2 is calculated:
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7. RGB Color Composition 2
Blue = C22
Green = C11 +2 Re(C12) + C22
Red = C11
RGB Color Composition 1
Blue = C11
Green = C11 +2 Re(C12) + C22
Red= C22
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8. • The “I” and “Q” values for each pixel are
supplied as 16 bit integers
• Converted to complex floating point
• Radiometric correction of data
• The calibration constant (KdB) is supplied
RH 0 (db) - Mumbai
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10. 3
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11. 3
• Select filter type to be applied:
• Boxcar Filter
• Refined Lee Filter
Select Filter
Size
Set the input and
output directories
Click Run to apply filter
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12. Unfiltered 5x5 Refined Lee Filtered
Crop of cFRS-1 mode acquired over Mumbai by RISAT - 1
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13. m-delta ( ) Decomposition:
)1(
2
sin1
2
sin1
0
0
0
mSfG
mSfR
mSfB
diffused
even
odd
m-chi ( ) Decomposition:
)1(
2
2sin1
2
2sin1
0
0
0
mSfG
mSfR
mSfB
diffused
even
odd
m-alpha ( ) Decomposition:
)1(
2
2cos1
2
2cos1
0
0
0
mSfG
mSfR
mSfB
volume
dihedral
surface
Cloude, et al., IEEE GRS Letters, 9(1), Jan 2012Raney et al., JGR, Vol. 117, E00H21, 2012
Stokes Parameters: (RH-RV Case, BSA Convention)
0
2
3
2
2
2
1
S
SSS
m
2
31
tan(deg)
S
S
0
3
2sin
mS
S
Degree of Polarization: Relative RH-RV Phase:
Degree of Circularity:
3
2
2
2
11
tan
2
1
(deg)
S
SS
Scattering mechanism:
Stokes Vector: (RH-RV Case) - BSA
22
1
22
0
RVRHS
RVRHS
*
3
*
2
2
2
RVRHS
RVRHS
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14. Decomposition techniques applied to cFRS-1 Image acquired over Mumbai by RISAT-1
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15. • Ratio of the Same Sense to
Opposite Sense echo
powers
• Indicator of the degree
of wavelength scale surface
and/or near subsurface
roughness
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16. 11 11 22 12
13 11 22 12
22 11 22 12
31 11 22 12
33 11 22 12
7 2Im
6
2Im
6
7 2Im
C J J J
C J J j J
C J J J
C J J j J
C J J J
CP
CP
CC
C
CC
C
3331
22
1311
0
00
0
Where :
Reflection and Rotation
symmetry assumptions
2221
1211*
)2/()2/(2/
2
1
*
JJ
JJ
kkJ
T
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18. Set Input Directory
Set Output Directory
Run the process
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19. Reference: Gunter Schreier(Ed.) SAR Geocoding Data and Systems (Publisher: Wichman)
• Nearest Neighbor
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21. Select the file to be geocoded
Any floating point bin file is supportedProvide the “infoRH.txt” file
This is in the directory where the product was
extracted
Choose the resampling method Run the process
Output will be automatically
opened in OpenEV
S-Curve Stretching gives good
visualization
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22. Geocoded C11 image of Mumbai, India
Acquired by RISAT-1 in cFRS-1 mode
15-NOV-2012
Scene Center
Longitude:72.930005
Latitude :19.220882
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23. ALOS L Band – Fully Polarimetric Image is used for this comparison
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24. Pauli RGB Freeman 3 component
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25. VanZyl 3 component Yamaguchi 3 component
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26. Arii ANNED 3 component Arii NNED 3 component
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27. Y4O Y4R
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28. G4U1 G4U2
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29. S4R Y4R
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30. Classified image of cFRS-1 scene acquired over Mumbai, India.
Urban Forest Mangroves Water Wetland
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31. Wishart Supervised Classification
Lee Refined Filter – 5x5 window
Co-Register datasets
Multilook to match other images
Import – C2 matrix
ALOS-PALSAR RADARSAT-2 TerraSAR-X RISAT-1
Sensors Band Polarization
Pixel
Size (m)
RISAT-1 C Hybrid Pol 2.5
RADARSAT-2 C Full Pol 8.0
ALOS-PALSAR L Full Pol 24.0
TerraSAR-X X HH and HV 3.0
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32. Classifier Wishart Classifier
Classes
TerraSAR-X
band Dual Pol.
RISAT-1-C band
Compact Pol
RADARSAT-C
band simulated
Compact Pol
RADARSAT-C
band Full Pol.
ALOS-PALSAR-L
band Full Pol.
Water 99.66 81.03 100 100 87.94
Mangroves 74.93 88.06 77.49 89.86 91.78
Urban 98.17 91.53 97.03 98.63 100
Forest 60.26 81.00 77.42 82.19 93.49
Saltpan 63.77 68.6 95.89 96.38 85.99
Wetland 89.96 89.96 97.77 97.54 73.44
Grassland 83.78 90.54 85.59 92.34 90.54
Accuracy % 81.84 83.40 89.96 93.40 89.38
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33. ALOS-PALSAR RADARSAT-2 TerraSAR-X RADARSAT-2 RISAT-1
Full pol Full Pol Dual Pol Simulated Hyb. Hyb.pol
Water Mangroves Urban Forest Saltpans Wetland Grassland
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34. Classification
SVM Wishart
Decomposition
m-δ m- SPAN CPR
Co-Register Datasets
5x5 Refined Lee Filter
Multilook to match resolution
3:3 Multilook 1:1 Multilook
Import and Prepare Data
RISAT-1 (Hybrid) RADARSAT-2 (Simulated Hyb)
Sensors Band Polarization
Pixel
Size (m)
RISAT-1 C Hybrid Pol 2.5
RADARSAT-
2
C Full Pol 8.0
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35. Class
RISAT-I RADARSAT-2
Wishart
m-δ,CPR,SPAN
(SVM)
m-χ,
CPR,SPAN
(SVM)
Wishart
m-δ,CPR-
SPAN (SVM)
m-χ,
CPR,SPAN
(SVM)
Water 78.59 84.32 84.00 99.95 98.91 99.05
Mangrov
es
60.60 88.92 88.99 75.25 71.85 70.72
Urban 56.74 60.99 63.43 91.73 92.06 92.25
Forest 56.15 81.54 81.54 52.87 54.94 56.88
Wetland 60.22 84.22 84.78 97.09 97.75 98.26
Overall
Acc.%
64.17 81.53 81.86 81.35 80.68 80.91
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36. Classified RISAT-1 image acquired over
Mumbai in cFRS-1 mode.
SVM classifier used on m-
decomposed image along with SPAN
and CPR
Urban Forest Mangroves Water Wetland
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37. Convert RISAT to Pseudo Full pol
Signature analysis Backscatter Analysis
Wishart Classification
Intensity Complex
Co-Register Datasets
5x5 Refined Lee Filter
Multilook to reduce speckle
2:2 Multilook
Import and Prepare Data
RISAT-1 (HybridPol) RADARSAT-2 (Full Pol)
Sensors Band Polarization
Pixel
Size (m)
RISAT-1 C Hybrid Pol 2.5
RADARSAT-
2
C Full Pol 8.0
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38. Variation of H/AlphaMean and Standard Deviation of σ0
• Urban features: hybrid σ0 shows higher value with more standard deviation
• Urban features can be clearly discriminated
• The other features are not discriminable – low dynamic range (within 3dB)
• Channel Imbalance observed
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39. Linear Pol
FCC RGB
Hybrid Pol
FCC RGB
LinearComplexLinearIntensity
HybridComplexHybridIntensity
Urban
Water
Forest
Mangroves
Wetland
Field Work
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40. Polarization
/Class
Linear(HH,HV)
Intensity
Circular(RH,RV)
Intensity
Linear(HH, HV)
Complex
Circular(RH,RV)
Complex
Urban 70.77 85.27 70.68 82.50
Forest 73.50 52.95 78.85 87.41
Water 95.19 99.45 94.99 99.83
Mangroves 80.09 59.78 82.88 91.74
Wetland 62.57 91.55 73.11 98.24
Accuracy
(%)
83.48 79.60 86.38 91.79
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41. RADARSAT-2 RISAT-1
Urban_Copol
Urban_Crospol
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42. RADARSAT-2 RISAT-1
Water_Copol
Water_crosspol
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43. Saoner Test Site
• Scene center:
• 21 23′09″N Latitude
• 78 55′12″E Longitude
• Major Crops:
• Paddy
• Sugarcane
• Wheat
• Gram
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44. 0%
57.5%
0%
41.4%
m-delta m-chi m-alpha
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45. Thank
You!
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