SA$^{2}$Net: Ship Augmented Attention Network for Ship Recognition in SAR Images
Yuanzhe Shang, Wei Pu, Danling Liao, Yang Ji, Congwen Wu, Yulin Huang, Yin Zhang, Junjie Wu, Jianyu Yang, Jianqi Wu · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · 2023
Maritime surveillance is extensively concerned by worldwide authorities, in which ship recognition in synthetic aperture radar (SAR) images is a significant and fundamental component. Though some development has been achieved in the SAR ship recognition task, two areas remain inadequately explored, which are the comprehensive utilization of multi-scale features and the deployment of the prior knowledge of the ship shape. In this paper, a novel ship augmented attention network (SA2Net) for ship recognition is proposed, which comprehensively utilizes the multi-scale features and integrates the ship shape prior to the end-to-end network. On one hand, due to the unequal effects of different scales, a scale attention module (SAM) is proposed to adaptively select and assign weights to desired feature scales while disregarding irrelevant scales. Moreover, a feature weaving module (FWM) is constructed to merge semantic and detailed features produced by the high-to-low backbone, enriching representations across all scales of ship targets. On the other hand, in order to incorporate the priory knowledge of the ship shape into the network, we develop a feature augmentation module (FAM) to further boost the ship recognition accuracy. This module can provide rectangular receptive fields that align with the shape of ships, wherein a limitation encountered with traditional square convolutions. Comprehensive experiments on representative three- and six-category OpenSARShip tasks and seven-category FUSAR-Ship tasks show that our SA2Net demonstrates superior performance when compared to the current state-of-the-art methods.