Speckle Reduction of Polarimetric SAR Images Based on Neural ICA

The polarimetric synthetic aperture radar (PSAR) images are modeled by a mixture model that results from the product of two independent models, one characterizes the target response and the other characterizes the speckle phenomenon. For the scene interpretation, it is desirable to separate between...

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Bibliographic Details
Published inNeural Information Processing pp. 387 - 393
Main Authors Ji, Jian, Tian, Zheng
Format Book Chapter
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2006
SeriesLecture Notes in Computer Science
Subjects
Blind Source Separation
Independent Component Analysis
Synthetic Aperture Radar
Synthetic Aperture Radar Image
Online AccessGet full text
ISBN9783540464815
3540464816
ISSN0302-9743
1611-3349
DOI10.1007/11893257_43

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Abstract The polarimetric synthetic aperture radar (PSAR) images are modeled by a mixture model that results from the product of two independent models, one characterizes the target response and the other characterizes the speckle phenomenon. For the scene interpretation, it is desirable to separate between the target response and the speckle. For this purpose, we proposed a new speckle reduction approach using independent component analysis (ICA) based on statistical formulation of PSAR image. In addition, we apply four ICA algorithms on real PSAR images and compare their performances. The comparison reveals characteristic differences between the studied neural ICA algorithms, complementing the results obtained earlier.
AbstractList The polarimetric synthetic aperture radar (PSAR) images are modeled by a mixture model that results from the product of two independent models, one characterizes the target response and the other characterizes the speckle phenomenon. For the scene interpretation, it is desirable to separate between the target response and the speckle. For this purpose, we proposed a new speckle reduction approach using independent component analysis (ICA) based on statistical formulation of PSAR image. In addition, we apply four ICA algorithms on real PSAR images and compare their performances. The comparison reveals characteristic differences between the studied neural ICA algorithms, complementing the results obtained earlier.
Author Tian, Zheng
Ji, Jian
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Chan, Lai-Wan
Wang, Jun
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Snippet The polarimetric synthetic aperture radar (PSAR) images are modeled by a mixture model that results from the product of two independent models, one...
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StartPage 387
SubjectTerms Blind Source Separation
Independent Component Analysis
Synthetic Aperture Radar
Synthetic Aperture Radar Image
Title Speckle Reduction of Polarimetric SAR Images Based on Neural ICA
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