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Speckle Reduction of Polarimetric SAR Images Based on Neural ICA

  • Jian Ji
  • Zheng Tian
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4233)

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.

Keywords

Independent Component Analysis Synthetic Aperture Radar Independent Component Analysis Synthetic Aperture Radar Image Blind Source Separation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jian Ji
    • 1
  • Zheng Tian
    • 1
    • 2
    • 3
  1. 1.Department of Computer Science & TechnologyNorthwestern Polytechnical UniversityXi’anChina
  2. 2.Department of Applied MathematicsNorthwestern Polytechnical UniversityXi’anChina
  3. 3.Key Laboratory of Education Ministry for Image Processing and Intelligent ControlHuazhong University of Science & TechnologyWuhanChina

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