摘要
本文提出一个新的最大似然(ML)分类算法对多视全极化合成孔径雷达(SAR)图象进行分类,给出了应用NASA/JPL机载L波段四视全极化SAR实测数据的试验结果,证明了新算法的有效性。此外,本文还将所提算法应用于部分的多视全极化SAR数据中,实现了地貌类型分类的极化通道优化。
In this paper, a new maximum likelihood (ML) classification algorithm is proposed to classify the multi-look polarimetric synthetic aperture radar (SAR) imagery. Experimental results with the NASA/JPL airborne L-band 4-look polarimetric SAR data demonstrate the effectiveness of the new algorithm. Furthermore, when using the algorithm in the classifications with subsets of the multi-look polarimetric SAR data, the polarization-channel optimization for the terrain type classification is implemented.
基金
电子科技大学
意大利Alenia Spazio在星载合成孔径雷达领域的科技合作课题
关键词
多视处理
相干斑
极化通道优化
SAR雷达
雷达
Polarimetric SAR, Multi-look processing, Speckle, Classification, Polarization-channel optimization
