An Integrated Method of Ship Detection and Recognition in Sar Images based on Deep Learning

Abstract

Ship target interpretation in SAR images has become an important topic in research in recent years. With the improvement of SAR image resolution, the performance of traditional automatic target recognition (ATR) method decreases gradually. The emergence of deep network provides a new solution for SAR image ship interpretation. Ship target interpretation in SAR images based on deep learning is divided into detection and classification, but they haven't been integrated yet. Based on the process flow of traditional ATR system, an integration method of ship target detection and recognition based on deep network is proposed in this paper. And at the end of the network, the squeeze-and-excitation (SE) module is added to the classification subnetwork. The effectiveness of the proposed integration method is verified by experiments, and the classification accuracy of the ship increased by 3.7% after adding SE module.