Polarimetric SAR Image Super-Resolution VIA Deep Convolutional Neural Network

Abstract

In order to solve the problem of full-polarimetric SAR image degradation, this paper proposes a full-polarimetric SAR image super-resolution reconstruction method combined with a convolutional neural network and residual compensation. Through the advantages of the deep convolutional neural network for nonlinear model fitting, this paper performs super-resolution reconstruction on low-resolution full-polarimetric SAR images, and then applies residual compensation to network reconstruction results, using low-resolution image information to the network. The super-resolution reconstruction results are corrected to obtain a high-resolution full-polarimetric SAR image. Compared with the traditional full-polarimetric SAR image super-resolution reconstruction method, the proposed method shows excellent results in both visual and quantitative evaluation indicators, especially the reconstruction of detailed information.