基于压缩多信号分类算法的森林区域极化SAR层析成像

Polarimetric SAR Tomography for Forested Areas Based on Compressive Multiple Signal Classification

该文研究了一种基于压缩多信号分类算法的森林区域极化SAR层析成像方法。其具体步骤包括:全极化的SAR接收成像区域的反射回波,利用各极化通道的信号建立多观测向量模型;应用小波基对高程向结构进行稀疏表示,采用压缩多信号分类算法对观测区域的高程向后向散射系数进行重建,实现对森林区域层析成像。最后,通过仿真实验、Pol SARpro仿真数据和德宇航E-SAR的P-波段数据验证了该方法在同等测量精度的要求下可以有效减少SAR层析成像所需的航过数,同时降低了虚假目标的出现概率。

This paper focuses on the polarimetric SAR tomography for forested areas based on compressive MultipleSignal Classification （MSC）. First, full polarimetric SAR receives the reflected echo of the imaging area. Then, the signals from polarimetric channels are used to build multiple measurement vector model, and a wavelet basis is used in order to sparsely represent vertical structure. For achieving the measurement of forested area, the backscattering coefficients are reconstructed by Compressive Multiple Signal Classification （CMSC） algorithm. Simulated data from PolSARpro software and P-band data acquired by the E-SAR sensor of the German Aerospace Center validate that the method can effectively reduce the passes for SAR tomography and the probability of occurrence of spurious spikes under the same measurement accuracy.