基于属性散射信息的随机梯度最小方差追踪SAR超分辨重建算法 postprint

Abstract: The applications in both the military and civil field have pressing needs and great expectations of achieving the target higher resolution and more detailed description. This paper firstly modeled the object-level SAR observations based on attributed scattering center (ASC) model in sparse representation framework. Secondly, it proposed a classifying strategy of the target attributes space for the object-level reconstruction in signal domain. Combined with data extrapolating, then it proposed a stochastic gradient minimum variance pursuit (SGMVP) based object-level super-resolution reconstruction algorithm. It finally achieved super-resolution image by FFT to effectively promotethe efficiency of the proposed algorithm. The proposed algorithm not only can achieve improved superresolution image but also provide accurate physically-relevant attributed features of the scatterers simultaneously. Experimental results confirm the effectiveness of the proposed algorithm.