Refocusing of Ship Target under Three-Dimensional Rotating in SAR Based on Complex-Valued Deep Learning

Abstract

In synthetic aperture radar (SAR) images, ship targets are defocused due to three-dimensional rotation, which affects subsequent SAR target detection and recognition tasks. This paper proposes a complex-valued convolutional neural network (CV-CNN) structure called CV-RefocusNet to refocus SAR three-dimensional rotating ship targets. CV-RefocusNet includes two parts of feature extraction network and image reconstruction network and adopts an end-to-end design method. To make full use of the amplitude and phase information of complex SAR images, the convolutional layer, deconvolutional layer, and activation function in CV-RefocusNet are all extended to the complex domain. Then refocusing experiments on simulated SAR data and GF-3 SAR data show that CV-RefocusNet could further improve the focus accuracy instead of real-value CNN (RV-CNN) with the same degree of freedom.