多视极化合成孔径雷达图象的分类和极化通道优化
CLASSIFICATION OF MULTI-LOOK POLARIMETRIC SAR IMAGERY AND POLARIZATION CHANNEL OPTIMIZATION
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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。Abstract: 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.
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