classification accuracy for risat-1 hybrid polarimetric data

APSAR 2013 Tsukuba Japan - Sep. 23 - 27, 2013.

Classification Accuracy for RISAT-1 Hybrid Polarimetric Data

Varsha Turkar1, Shaunak De1, G. G. Ponnurangam1, Rinki Deo1, Y.S. Rao1 and Anup Das2

1 CSRE, Indian Institute of Technology Bombay2 Space Application Center, ISRO


Introduction• Studies on compact and hybrid polarimetric SAR data is

currently in focus.• Primary reasons: [1]

• Wider swath than Full-Pol mode• Low PRF requirement – less demanding on hardware• Higher incidence angle range coverage

• Studies demonstrated with compact-pol: [2]

• Crop classification• Soil moisture estimation• Ship detection and sea-ice classification

[1] R.K. Raney, “Hybrid-polarity SAR architecture”, IEEE Trans. Geosci. Remote Sens., 45(11): 3397 –3404, Nov. 2007[2] F.J. Charbonneau, B. Brisco, R.K. Raney, H. McNarin, P.W.Vachon, J.Shang, R. DeAbreu, C. Champagne, A. Merzouki and Geldsetzer, “Compact polarimetry overview and application assessment”, Can. J. Remote Sens., vol. 36, 2, pp. s298-s315, 2010.


RISAT-1 – The first compact pol SAR

• The work carried out so far has been based on simulated hybrid-pol data

• RISAT-1 – first spaceborne hybrid PolSAR system• Indigenously developed • C-band (5.35 GHz) hybrid polarimetric

SAR• Multi-polarisation and multi-resolution • 50m – 1m spatial resolution• RH/RV, HH/HV modes supported

• Right circular transmit and coherent linear receive mode (CTLR)

Courtesy: ISRO

Backscatter ( 0 ) Calculation

• SLC data is supplied as 16 bit integers

• Converted to complex floating point

• Radiometric correction of data• The calibration constant (KdB) is

supplied

Here:

RH 0 (db) - Mumbai

Export to C2 Matrix

𝐶=[ ⟨𝐸𝑅𝐻𝐸𝑅𝐻∗ ⟩ ⟨𝐸𝑅𝐻𝐸𝑅𝑉

∗ ⟩⟨𝐸𝑅𝑉 𝐸𝑅𝐻

∗ ⟩ ⟨𝐸𝑅𝑉 𝐸𝑅𝑉∗ ⟩ ]

• Two channel data – i.e. RH and RV

• Supplied as SLC data(complex) 16 bit integer values

• After conversion to float C2 is calculated:


Classification Methods

• Wishart supervised classifier• Compute the mean covariance matrix (C2) over the training areas

This is the mean covariance matrix for class .

• The complex Wishart distrubution is given by:

• The distance dm is computed for each pixel, for each class

• The pixel is assigned to the class with the minimum distance


Classification Methods

• SVM (Support Vector Machine)• It is based on search of optimal hyperplane which can separate the classes.

• The SVM makes the use of non-linear function which transforms the data from input space to higher dimension feature space so that the data can be linearly separable.

• Various kernels may be used:• Linear• RBF• Polynomial


Objectives of Study

• Backscattering coefficient ( 0σ ) for discrimination of various land features using both linear and hybrid polarimetric RISAT-1 data

• Compared classification accuracy using RADARSAT-2 simulated hybrid and RISAT-1 compact pol data


Study Area: Mumbai, India.

Scene CenterLongitude:72.930005Latitude :19.220882

RISAT-1RH/RV – FRS

15th Nov 2012




15th Nov 2012

Test site chosen is the metropolis of Mumbai, India.

The area consists of:• Built-up dense urban settlements• Moderately dense deciduous forest• Mangroves• Wetlands• Bare soil • Water• Grasslands

Urban AreasCourtesy:

indianexpress.com

Forest Mangroves




15th Nov 2012

Wetland / Saltpan

Bareland Water

Grassland


Data sets and Field data collection

RISAT-1 data has been acquired on two successive days over Mumbai.

Satellite Mode Date of Acquisition Incidence angle

RISAT-1 HH/HV 14th Nov 2012 49.3

RH/RV 15th Nov 2012 35.9

RADARSAT-2 Full Pol. 16th Feb 2011 41.73

Ground-truth parameters in terms of soil moisture, vegetation height and biomass, etc. were collected synchronous with the satellite passes.


RISAT-1BACKSCATTER ANALYSISComparison between RH/RV and HH/HV backscatter


MethodologyRISAT-1 Data

• cFRS-1 [RH/RV]• FRS-1 [HH/HV]

Pre-processing• Data extraction

• Calibration

Multilook • 3:3 in Range: Azimuth

Compute statistics for 6 test areas

Plot Backscattering Coefficient


RISAT-1 0 B Analysisԁ

Class HV HH RV RH

Grass-land -12.48 -4.13 -7.47 -3.35

Bare-land -14.67 -5.88 -10.56 -6.47

Water -17.62 -11.53 -15.12 -11.64

Mangroves -11.30 -3.28 -6.37 -2.79

Forest -12.76 -4.20 -7.18 -4.27

Urban -13.15 -1.87 -5.87 -0.28

Wetland -16.99 -10.57 -10.69 -9.54

AVERAGE 0 ԁB VALUES FOR LINEAR AND HYBRID MODE DATA


RISAT-1 0 B Analysis (Cont.)ԁ

Mean and standard deviation of σ0 dB of RISAT-1 linear and hybrid mode data for various classes.


RISAT-1 0 B Analysis (Cont.)ԁ

• There is a clear separation of mean 0 values of various classes

• Yet, we can not classify data on backscatter alone• Standard deviation of features is high• Overlaps with mean values of other features• Example: forest and mangrove class overlap

• The standard deviation from mean is consistent in all classes• Value ranging from 2.18 in water to 2.73 in the forest class• Exception: urban class - higher standard deviation of 4.32

• There is a 13.4o difference in the incidence angle between the RH/RV and HH/HV datasets from RISAT-1 : This may be the reason for the difference in mean σ0


RISAT-1 & RADARSAT-2CLASSIFICATIONComparison between hybrid and simulated hybrid data


Datasets

m-chi decomposed image for RISAT-1 - Mumbai city

RADARSAT-2 Mumbai area -Zyl decomposition


Hybrid Polarimetric Decompositions

m-δ m-χ m-


MethodologyImport and Prepare Data• RISAT-1

(HybridPol)• RADARSAT-2

(Full Pol)

Multilook to reduce speckle• 3:3 Multilook

5x5 Refined Lee Filter

Co-Register Datasets

Wishart Classification• Intensity • Complex

Decomposition• m-• m-• CPR

Classification and Analysis• SVM• Wishart


Combining CPR, SPAN and m-δ / m-

CPR SPAN

m-δ / m-

VolumeDoubleBounce

Surface

SVM


Combining CPR, SPAN and m-δ / m-

Histogram of CPR for test areas

• The CPR, SPAN and individual components of the m-δ / m- decompositions (Vol, Dbl, Surface) are normalized and used as input bands to the SVM classifier.

• CPR helps discriminate between mangroves and forest areas (see histogram)

• SPAN helps discriminate urban areas from background.


Classification Results – Test Area

Class

RISAT-1 Hybrid PolRISAT-1 Dual Pol

RADARSAT-2 Simulated Hybrid Pol

Wishartm-,

CPR,SPAN (SVM)

m-χ, CPR,SPAN

(SVM)Wishart Wishart

m-,CPR-SPAN (SVM)

m-χ, CPR,SPAN

(SVM)

Water % 100.00 100.00 100.00 65.15 100.00 100.00 100.00

Mangroves %

73.87 76.78 77.41 37.21 67.84 60.29 56.96

Urban % 78.64 91.56 97.85 69.95 75.25 71.03 73.43

Forest % 86.52 99.34 99.57 82.74 45.58 45.01 41.23

Wetland % 91.57 94.45 94.77 48.67 98.83 97.97 98.93

Grassland % 78.16 84.66 85.44 32.46 45.00 57.09 60.96

Overall User Acc. %

84.67 91.61 92.84 58.57 68.45 68.17 67.69

Classification accuracy for various land covers using test areas


Results and Analysis• Wishart supervised classifier:

• RISAT-1 – (RH/RV) • 80.62% for training areas • 84.67% for test areas.

• RADARSAT-2 (Simulated RH/RV)• 72.83% for training areas • 68.45% for test areas

• The classification accuracy increases by 7% after combining the three components of m-χ or m-δ with the CPR and SPAN [3] for RISAT-1.

• RISAT-1 hybrid polarimetric data performs better than RADARSAT-2 simulated hybrid polarimetric data for all three combinations.

• The lowest classification accuracy of 32.46% for the grassland class is due to its confusion with forest class.

[3] V. Turkar, Shaunak De, Y. S. Rao, A. Bhattacharya and A. Das, “Comparative Analysis Of classification Accuracy For RISAT-1 Hybrid Pol. Data”, Proc. IEEE IGARSS 2013, Melbourne.


Classified Images of RISAT-1

Classified image of RISAT-1 C-band Hybrid polarimetrc Mumbai data (a) Wishart supervised (b)SVM classified (m-χ + CPR + SPAN)

Wishart Supervised SVM (m-χ + CPR + SPAN)

Legend

Water

Mangroves

Forest

Urban

Wetland

Grassland


SVM Classified Images (m- + χ CPR + SPAN)

SVM (m-χ + CPR + SPAN) classified image of (a) RISAT-1 hybrid and (b) RADARSAT-2 simulated hybrid mode data.

Legend

Water

Mangroves

Forest

Urban

Wetland

Grassland

RISAT-1 RADARSAT-2


EFFECT OF TRAINING AREA SELECTIONComparison of classification RISAT-1 (RH/RV)


Classification – with different training areas

Classification accuracy for various land covers using test areas

Class

RISAT-1 Hybrid PolLarge Training Area

RISAT-1 Hybrid PolSmall Training Area

Wishart Wishart

Water % 100.00 100.00

Mangroves % 73.87 84.67

Urban % 78.64 91.66

Forest % 86.52 76.47

Wetland % 91.57 93.16

Grassland % 78.16 88.79

Overall User Acc. % 84.67 89.12


Classified Image - RISAT-1 (RH/RV)

Water Mangrove Urban Forest Wetland Grassland

Producer's

Accuracy

Water 100 0 0 0 0 0 100

Mangrove 0 79.81 7.93 12.26 0 0 79.81

Urban 0 5.32 94.19 0.3 0.07 0.12 94.19

Forest 0 9.13 0.63 84.17 0.29 5.78 84.17

Wetland 0 0 0 0 95.8 4.2 95.80

Grassland 0 0 0 13.33 6.67 80.00 80.00

User Accuracy 100 84.67 91.66 76.47 93.16 88.79 89.12

Homogeneous Training Areas


Confusion Matrix –Small training areas

RISAT-1 RH/RV Wishart

Class Urban Forest Mangroves Water Wetland

Urban 97.95 0.34 0.73 0 0

Forest 0.28 88.33 11.2 0 1.59

Mangroves 1.78 9.9 88.06 0 0

Water 0 0 0 100 1.59

Wetland 0 1.43 0 0 96.81

Overall Accuracy: 94.23


Confusion Matrix –Large training areas

RISAT-1 RH/RV Wishart

Class Urban Forest Mangroves Water Wetland

Urban 85.97 0 0 0.63 13.4

Forest 0 92.59 0.71 6.7 0

Mangroves 0 13.42 85.58 1 0

Water 0 8.12 0.09 91.44 0.35

Wetland 12.78 0 0 2.3 84.92

Overall Accuracy: 88.10


Conclusion• Mean and standard deviation values follow the same trend for both the

imaging modes: linear and hybrid

• Urban class exhibits higher standard deviation from mean

• The horizontally polarized receive components, HH and RH are higher than their respective vertically polarized receive components, HV and RV

• The performance of hybrid polarimetric (RH,RV) data in terms of classification accuracy is better than dual polarization (HH, HV) data

• The classification accuracy increases by combining three components (surface, double and volume) of m- or m- along with CPR and SPAN for χ δRISAT-1 and RADARSAT-2 hybrid polarimetric data.

• RISAT-1 hybrid polarimetric data classification accuracy is better than simulated hybrid polarimetric data from RADATSAT-2.

classification accuracy for risat-1 hybrid polarimetric data

Engineering

tsukuba japan

hybrid mode data apsar

compact pol data apsar

data sets

data cfrs

channel data

hybrid polarimetric

field data collection