Information compression and speckle reduction for multifrequency polarimetric SAR imagery using KPCA

Ying Li, Xiao Gang Lei, Ben Du Bai, Yan Ning Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

Multifrequency Polarimetrie SAR imagery provides a very convenient approach for signal processing and acquisition of radar image. However, the amount of information is scattered in many images, and redundancies exist between different bands and polarizations. Similar to signal-polarimetric SAR image, multifrequency Polarimetrie SAR image is corrupted with speckle noise at the same time. This paper presents a method of information compression and speckle reduction for multifrequency Polarimetrie SAR imagery based on kernel principal component analysis (KPCA). KPCA is a nonlinear generalization of linear principal component analysis using kernel trick. The NASA/JPL Polarimetric SAR imagery of P, L, and C bands quadpolarizations is used for illustration. Experimental results show that KPCA has better capability in information compression and speckle reduction compared with linear PCA.

Original languageEnglish
Title of host publicationProceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007
Pages1688-1692
Number of pages5
DOIs
StatePublished - 2007
Event6th International Conference on Machine Learning and Cybernetics, ICMLC 2007 - Hong Kong, China
Duration: 19 Aug 200722 Aug 2007

Publication series

NameProceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007
Volume3

Conference

Conference6th International Conference on Machine Learning and Cybernetics, ICMLC 2007
Country/TerritoryChina
CityHong Kong
Period19/08/0722/08/07

Keywords

  • Despeckling
  • Information compression
  • Kernel PCA
  • Multifrequency polarimetrie SAR imagery

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