Positive-semidefinite Based Target Decomposition Using Optimal Model-matching with Polarization Similarity
Fan Qing-hui①② Lu Hong-xi③ Bao Zheng③ Xiao Chun-bao①②
①(School of Computer Science and Technology, Xidian University, Xi’an 710071, China) ②(Information Engineering School, Henan University of Science and Technology, Luoyang 410023, China) ③(National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China)
Target decomposition is an important tool to realize target classification, detection and recognition applications with Polarimetric SAR (PolSAR). However, the traditional method with priority of volume scattering component extraction seriously performs overestimation in the volume scattering energy or underestimation in the dihedral scattering energy. In this paper, by introducing polarimetric similarity measure, data-driven model- matching for basic scattering mechanism is proposed. On this basis, the priority of scattering mechanisms energy extraction is determined with the similarity measure. Based on the non-negative constraint of energy, all the orders of residual matrix are reextracted for the final energy contribution of the dihedral scattering, volume scattering, and surface scattering mechanism. The processing results of real data and their comparison with the optical image results show that the proposal is better than traditional methods for the accurate extracttion of the basic scattering characteristics in the targets region.
范庆辉,卢红喜,保铮,肖春宝. 基于半正定约束的极化相似度最优模型匹配目标分解[J]. 电子与信息学报, 2015, 37(8): 1821-1827.
Fan Qing-hui, Lu Hong-xi, Bao Zheng, Xiao Chun-bao. Positive-semidefinite Based Target Decomposition Using Optimal Model-matching with Polarization Similarity. JEIT, 2015, 37(8): 1821-1827.
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