TupinTU1T024.ppt

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


Index

Index

Introduction

Speckle filtering


Conclusion


Speckle filtering approaches


Index

Index

Introduction

Speckle filtering


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


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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AIRSAR ALOS/PALSAR Radarsat–2

Refined LEE

NL-PolSAR

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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.


Index

Index

Introduction

Speckle filtering


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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TupinTU1T024.ppt

Technology

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