INFORMATION COMPRESSION AND SPECKLE REDUCTION FOR MULTIFREQUENCY POLARIMETRIC SAR IMAGERY USING KPCA

摘要：

Multifrequency polarimetric 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 polarimetric SAR image is corrupted with speckle noise at the same time.This paper presents a method of information compression and speckle reduction for multifrequency polarimetric 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.

关键词： Kernel PCA Multifrequency polarimetric SAR imagery Information compression Despeckling

作者: YING LI XIAO-GANG LEI BEN-DU BAI YAN-NING ZHANG

作者单位: School of Computer Science, Northwest Polytechnical University, Xian, 710072, China

会议类型: 国际会议

会议名称: 2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

会议地点: 香港

会议语种:英文

页码: 1688-1692

在线出版日期: 2007-08-19（万方平台首次上网日期，不代表论文的发表时间）