基于球面散射相似性的POLSAR图像分类方法

针对基于H-Alpha平面的目标分类方法运算量偏大的问题，本文研究了基于散射相似性的极化合成孔径雷达（POLSAR）图像分类。首先在分析现有目标散射相似性度量参数的不足基础上，利用目标相干矩阵定义了目标散射相似性的新度量参数。该参数综合考虑了目标主散射机制、次要散射机制与规范目标的相似性，以及不同散射机制对应的发生概率，因而它能准确反映目标与规范目标的散射相似程度。接着，考虑到表面散射在自然界中是一种普遍的散射类型，利用新参数提取了表面散射相似性参数，并从理论上分析了该参数表征目标散射类型的可行性及它与平均Alpha角、极化散射熵的约束关系。最后，利用San Francisco地区AIRSAR极化数据验证了表面散射相似性替代平均Alpha角的有效性，并讨论了表面散射相似性参数分别与现有极化散射熵替代参数的组合分类效果。实验结果表明，基于简单线性近似参数+表面散射相似性的H-Alpha替代方法更具实用性。 

Classification for POLSAR Images Based on Surface Scattering Similarity

Aiming at the deficiency of the classification based on H-Alpha polarimetric decomposition theorem, we study the classification for POLSAR images based on scattering similarity. Firstly, a new parameter is proposed to measure the scattering similarity between targets. As this parameter contains all scattering similarities between the scattering mechanisms of a distributed target and canonical mechanism, it describes accurately the degree of average scattering similarity between a distributed target and a canonical target. As an application, we analyze the feasibility of the scattering similarity parameter corresponding to the sphere scattering in the extraction of average scattering theoretically. Furthermore, we also discuss the classification scheme with the scattering similarity corresponding to the sphere scattering and the polarimetric scattering entropy and give the boundary of the effective classification region theoretically. With the AIRSAR data of San Francisco, the veracity of scattering similarity in measuring the degree of average scattering similarity and the effective of the scattering similarity corresponding to the sphere scattering are validated.