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

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

科研成果: 书/报告/会议事项章节会议稿件同行评审

6 引用 (Scopus)

摘要

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.

源语言英语
主期刊名Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007
1688-1692
页数5
DOI
出版状态已出版 - 2007
活动6th International Conference on Machine Learning and Cybernetics, ICMLC 2007 - Hong Kong, 中国
期限: 19 8月 200722 8月 2007

出版系列

姓名Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007
3

会议

会议6th International Conference on Machine Learning and Cybernetics, ICMLC 2007
国家/地区中国
Hong Kong
时期19/08/0722/08/07

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