Transcript Speckle filtering

INFLUENCE OF SPECKLE
FILTERING OF POLARIMETRIC
SAR DATA ON DIFFERENT
CLASSIFICATION METHODS
Fang Cao1, Charles-Alban Deledalle1, JeanMarie Nicolas1, Florence Tupin1, Loïc Denis2,
Laurent Ferro-Famil3, Eric Pottier3, Carlos
López-Martínez4
1 Institut
Télécom, Télécom ParisTech, France
2 Université de Lyon, France
3 Université de Rennes 1, France
4 Universitat Politècnica de Catalunya, Spain
Tuesday, 26/07/2011, Vancouver, Canada, IGARSS 2011
Index
Index
Introduction
Speckle filtering
Decomposition and classification
Conclusion
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Introduction
 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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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
Refined Lee
IDAN
NL-PolSAR
|SHH- SVV| |SHV| |SHH+ SVV|
San Francisco (JPL L-Band AIRSAR)
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Speckle filtering approaches
Refined Lee
IDAN
|SHH- SVV| |SHV| |SHH+ SVV|
Flevoland (JPL L-Band AIRSAR)
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NL-PolSAR
Index
Index
Introduction
Speckle filtering
Decomposition and classification
Conclusion
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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.
Coherency matrix:
Hermitian, semi-definite positive matrix → diagonalization
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Cloude-Pottier Decomposition
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
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1.0
b
Entropy
0
90°
Alpha
0
1.0
Anisotropy
0
Refined Lee
IDAN
San Francisco by JPL L–Band AIRSAR
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NL-PolSAR
1.0
b
Entropy
0
90°
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.
Alpha
0
1.0
Anisotropy
0
Refined Lee
IDAN
San Francisco by JPL L–Band AIRSAR
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NL-PolSAR
The Wishart H  Classification
POLSAR data
Speckle reduction
Building
Forest
Cloude-Pottier decomposition
Initialization for 8 classes using H/α
Wishart clustering
No
Water
Convergent?
Classification results
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H/ initialization: 8 classes
The Wishart H /  Classification
Wishart clustering
•
Supervised algorithm
•
Based on the complex Wishart distribution of coherency matrix
•
Use maximum likelihood criterion
Distance measure
V : the cluster center coherency matrix
Maximum likelihood criterion
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: the trace of a matrix
Decomposition and classification
Refined LEE
NL-PolSAR
AIRSAR
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ALOS/PALSAR
Radarsat–2
Decomposition and classification
Refined LEE
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
NL-PolSAR
sensor.
AIRSAR
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ALOS/PALSAR
Radarsat–2
The NL-PolSAR filter has better performance than the refined Lee filter, for example, the
golf 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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