Super-Resolution Reconstruction of Multi-Polarization Sar Images Based on Projections Onto Convex Sets Algorithm
Jin Huang, Bo Gao, Yan Chen, Yunping Chen, Ling Tong · 2018
Resolution is one of the important indices to measure the quality of SAR images. Super-resolution reconstruction is a widely adopted resolution enhancement method. Many algorithms have been developed for the super-resolution reconstruction. Among these algorithms, this paper applies projections onto convex sets algorithm to SAR image reconstruction processing. The POCS can efficiently obtain high-resolution SAR images with enhanced details. However, the POCS requires many low-resolution SAR images of the same area to gain a better result, usually 10 to 20 images. Such requirement is very difficult to achieve when only single-polarization mode is included. In this paper, we propose a novel method that utilizes all the polarimetric images of the same original SAR data for the algorithm. Thus, the number of the available images is increased exponentially. The experiment results have demonstrated the effectiveness of our proposed method: The reconstructed high-resolution SAR image based on multi-polarimetric information is more detailed and clearer than that based on single-polarization information.