分量模型和散射参数的全极化雷达图像分类

在分析了典型的极化目标分解和地物分类算法基础上,提出了融合Yamaguchi分解和H/α(H为散射熵,α为地物散射角)平面分解结果的迭代处理目标分类方法.首先,通过获取4种散射分量及地物的散射熵和散射角,结合6个参量,将极化合成孔径雷达图像中的地物初始分类;然后,利用相干散射矩阵服从Wishart分布的特性进行迭代,获得最终分类结果.实验结果证明,该算法提高了分类性能,运算量小,分类效果较好.

Full Polarimetric SAR Classification Based on Four-component Decomposition Model and Scattering Parameters

Based on the analysis of typical polarized target decomposition and classification,the paper proposes a new scheme for iterative classification of polarimetric SAR image,which blends the outcomes of Yamaguchi decomposition and H/α decomposition.This technique extracts four decomposition coefficients of four scattering mechanism components through Yamaguchi decomposition,the scattering entropy and angle through H/α decomposition first;then the initial classification of the POLSAR images is done by the combination of the 6 parameters mentioned above.The final result is obtained by iterative classification due to coherence scattering matrix following wishart distribution.The classification performance improved,better effectiveness and less amount of computation is demonstrated by the experimental results of polarimetric SAR data.