Dual-Polarization SAR Ship Target Recognition Based on Mini Hourglass Region Extraction and Dual-Channel Efficient Fusion Network

Abstract

A novel Dual-polarization SAR ship target recognition method based on feature and loss fusion deep network is proposed in this paper, to improve the generalization ability, the recognition accuracy of imbalance samples, and the real-time performance of the general deep learning network. The proposed combined network is composed of two parts. The first part is the target region extraction network based on lightweight mini Hourglass network, to eliminate the impact of data imbalance and background noise on the identification accuracy. The second part is a two-channel feature/loss function fusion network based on Efficient B2 backbone network, aiming to solve the problem of dual-polarization image feature fusion and iterative convergence acceleration. The proposed method is tested on SAR image ship slices from the OpenSAR data sets. The experimental results indicates that, the proposed method achieves a recognition rate of 110FPS with recognition accuracy of 87.72%, and exceeds the SOTA by recognition accuracy 3.72% with convergence speed improved by 75.68%. The proposed method can be applied to SAR target recognition with dual polarization, imbalance samples and low-resolution condition for reference.