tupintu1t024.ppt
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Tuesday, 26/07/2011, Vancouver, Canada, IGARSS 2011
INFLUENCE OF SPECKLE FILTERING OF POLARIMETRIC SAR DATA ON DIFFERENTCLASSIFICATION METHODS
Fang Cao1, Charles-Alban Deledalle1, Jean-Marie Nicolas1, Florence Tupin1, Loïc Denis2, Laurent Ferro-Famil3, Eric Pottier3, Carlos López-Martínez4
1 Institut Télécom, Télécom ParisTech, France2 Université de Lyon, France3 Université de Rennes 1, France4 Universitat Politècnica de Catalunya, Spain
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Index
Index
Introduction
Speckle filtering
Decomposition and classification
Conclusion
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Speckle filtering:• A pre-processing step to reduce the speckle noise
before image segmentation or classification
Tested filters: Refined Lee’s filter, IDAN filter and NL-PolSAR filter
Decomposition and classification:
Evaluation of the performance of speckle filtering methods through Cloude–Pottier decomposition and Wishart H/alpha classification
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IntroductionIntroduction
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Index
Index
Introduction
Speckle filtering
Decomposition and classification
Conclusion
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Speckle filtering approaches
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Speckle filtering approaches
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Speckle filtering approaches
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Speckle filtering approaches
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Speckle filtering approaches
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Speckle filtering approaches
San Francisco (JPL L-Band AIRSAR)
Refined Lee IDAN NL-PolSAR
|SHH- SVV| |SHV| |SHH+ SVV|
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Speckle filtering approaches
Flevoland (JPL L-Band AIRSAR)
Refined Lee IDAN NL-PolSAR
|SHH- SVV| |SHV| |SHH+ SVV|
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Index
Index
Introduction
Speckle filtering
Decomposition and classification
Conclusion
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Coherency matrix: Hermitian, semi-definite positive matrix → diagonalization
Cloude-Pottier Decomposition
• Eigenvalue/eigenvector calculation of the coherency matrix of fully polarimetric SAR data.
• Covering the whole range of scattering mechanisms
• Automatically basis invariant.
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Probability of each 3 scattering mechanism
Entropy H: the global distribution of scattering mechanism
angle: the type of scattering mechanism
Anisotropy A : the two least important scattering mechanism effects
Cloude-Pottier Decomposition
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b
Refined Lee IDAN NL-PolSARSan Francisco by JPL L–Band AIRSAR
Entropy
Alpha
Anisotropy
1.0
0
1.0
0
90°
0
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Entropy
Alpha
Anisotropy
1.0
0
1.0
0
90°
0
b
Refined Lee IDAN NL-PolSAR
The refined Lee filter and the NLPolSAR filters have similar performance. The IDAN filter usually introduces bias in entropy and anisotropy values, which may result to unreliable classification results.
San Francisco by JPL L–Band AIRSAR
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The Wishart H Classification
Building
Water
Forest
Initialization for 8 classes using H/α
Wishart clustering
Convergent?
Cloude-Pottier decomposition
Speckle reduction
No
POLSAR data
Classification resultsH/ initialization: 8 classes
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Wishart clustering
• Supervised algorithm
• Based on the complex Wishart distribution of coherency matrix
• Use maximum likelihood criterion
: the trace of a matrix
The Wishart H / Classification
Distance measure
V : the cluster center coherency matrix
Maximum likelihood criterion
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Decomposition and classification
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AIRSAR ALOS/PALSAR Radarsat–2
Refined LEE
NL-PolSAR
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Decomposition and classification
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AIRSAR ALOS/PALSAR Radarsat–2
Refined LEE
NL-PolSAR
The results of AIRSAR, ALOS/PALSAR and RadarSat-2 data show that the classification results with different sensors are quite similar, except the water area in the AIRSAR data, which is due to the big variation of the incidence angle of the airborne sensor.
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The NL-PolSAR filter has better performance than the refined Lee filter, for example, thegolf course areas and the lakes in the AIRSAR classification results.
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Index
Index
Introduction
Speckle filtering
Decomposition and classification
Conclusion
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Conclusion
• Comparison of 3 speckle filters:
Refined Lee’s filter, IDAN filter and the NL-PolSAR filter• Comparison of the influence on decomposition and
classification
Cloude-Pottier decomposition & Wishart H/a classification
• Obtained results with different sensors:
Radarsat-2, ALOS/PALSAR and AIRSAR• The NL-PolSAR filter achieves the best performance
in our experimental tests
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Thank you!
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