基于紧致字典的基追踪方法在SAR图像超分辨中的应用

Super-Resolution Processing of SAR Images by Basis Pursuit Method Based on Compacted Dictionary

基追踪方法是信号稀疏表示领域的一种新方法.它寻求从超完备的基集合(字典)中得到信号的最稀疏的表示,即用尽可能少的基尽可能精确地表示原信号,从而获得信号的内在本质特性.本文将基追踪方法的应用扩展到SAR(Synthetic Aperture Radar)图像的超分辨问题上来.首先在相位历史域依据SAR目标属性散射模型构造了一类紧致字典,从而大大减小了所求解问题的维数,其次设计了一种新的迭代算法进行快速求解,得到SAR图像中各散射中心位置和幅度的高精度估计,最后依据相位历史域SAR目标属性散射模型,生成更大尺度的相位历史数据,对生成的相位历史数据成像即得到更高分辨率的SAR图像.仿真算例和MSTAR实测数据计算表明,基于紧致字典的基追踪方法能够快速稳定实现,同时具有良好的超分辨性能.

Basis Pursuit is a novel method of signal sparse representation. It seeks sparse representation from over-complete dictionaries. That is to use the fewest dictionary elements to represent the signal exactly. Then the intrinsic feature of the signal can be captured. We extend the application of the Basis Pursuit method to SAR super-resolution processing. Firstly, based on the SAR attributed scattering model, a new type compacted Dictionary is designed in phase history domain. By this way, the dimension of the desired problem is even smaller. Secondly, a new and fast iterative algorithm is proposed,the fine feature parameter estimation of the scatter in SAR image is obtained. Finally, in terms of SAR attributed scattering model in the phase history domain,larger scale phase history data is built. By FFT imaging, higher resolution image is obtained. Simulation experiments and computational results of measured MSTAR data demonstrate that Basis Pursuit can be implemented speedy and stably,it can provide super-resolution at the same time.