Polarimetric SAR Tomography for Forested Areas Based on Compressive Multiple Signal Classification
Zhang Bing-chen①② Wang Wan-ying①②③ Bi Hui①②③ Zhao Yao①② Hong Wen①②
①(Science and Technology on Microwave Imaging Laboratory, Beijing 100190, China) ②(Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China) ③(University of Chinese Academy of Sciences, Beijing 100190, China)
Abstract：This paper focuses on the polarimetric SAR tomography for forested areas based on compressive Multiple Signal 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.
张冰尘, 王万影, 毕辉, 赵曜, 洪文. 基于压缩多信号分类算法的森林区域极化SAR层析成像[J]. 电子与信息学报, 2015, 37(3): 625-630.
Zhang Bing-Chen, Wang Wan-Ying, Bi Hui, Zhao Yao, Hong Wen. Polarimetric SAR Tomography for Forested Areas Based on Compressive Multiple Signal Classification. , 2015, 37(3): 625-630.
2015, Vol. 37 
