基于壓縮多信號分類算法的森林區(qū)域極化SAR層析成像
doi: 10.11999/JEIT140584 cstr: 32379.14.JEIT140584
基金項目:
國家973計劃項目(2010CB731905)和中國科學(xué)院創(chuàng)新團隊國際合作伙伴計劃先進微波探測與信息處理資助課題
Polarimetric SAR Tomography for Forested Areas Based on Compressive Multiple Signal Classification
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摘要: 該文研究了一種基于壓縮多信號分類算法的森林區(qū)域極化SAR層析成像方法。其具體步驟包括：全極化的SAR接收成像區(qū)域的反射回波，利用各極化通道的信號建立多觀測向量模型；應(yīng)用小波基對高程向結(jié)構(gòu)進行稀疏表示，采用壓縮多信號分類算法對觀測區(qū)域的高程向后向散射系數(shù)進行重建，實現(xiàn)對森林區(qū)域?qū)游龀上瘛Ｗ詈?，通過仿真實驗、PolSARpro仿真數(shù)據(jù)和德宇航E-SAR的P-波段數(shù)據(jù)驗證了該方法在同等測量精度的要求下可以有效減少SAR層析成像所需的航過數(shù)，同時降低了虛假目標的出現(xiàn)概率。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.
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