基于极化干涉相似性参数的四元素分解

该文针对传统极化合成孔径雷达(PolSAR)分解方法过高估计植被成分的问题，提出一种基于极化干涉相似性参数(PISP)的极化干涉分解方法。利用极化干涉合成孔径雷达(PolInSAR)的3组最优相干散射机制定义的PISP具有对地表散射体空间分布敏感的特性和旋转不变性。该文基于PISP的物理意义对植被模型进行改进，使利用该模型分解相干矩阵得到的不同地物体散射功率具有自适应性。最后利用欧空局(DLR)E-SAR获取的L波段全极化干涉数据验证该分解方法的有效性，实验结果表明，该算法得到的分解结果能有效区分植被和建筑物。

Four-component Decomposition Based on Polarimetric Interferometric Similarity Parameter

An improved four-component decomposition method is proposed based on Polarimetric Interferometric Similarity Parameter (PISP). This method can solve the vegetation component overestimation problems with traditional polarimetric SAR decomposition. The PISP is calculated by three optimized mechanisms obtained from PolInSAR datasets. Therefore, it is sensitive to the dimensional distribution of terrain target, and rotation invariant. The proposed method uses the PISP to improve the volume model. With the improved model, the volume scattering power of different terrain target can be calculated adaptively. The effectiveness of the proposed method is demonstrated with German Aerospace Centers (DLR) E-SAR L-band PolInSAR datasets. The experiment result shows that the building and forest can be well distinguished with the proposed method.