DFWA-Net: Dual-Domain Feature-Enhanced With Wavelet Attention Network for SAR Ship Detection

Abstract

Synthetic aperture radar (SAR) is a high-resolution remote sensing technology widely employed for ground and sea surface target detection. However, due to the unique imaging mechanism and information representation of SAR images, conventional spatial-domain feature extraction methods often struggle to fully capture their discriminative features. To address this limitation, this letter introduces the wavelet domain as an additional feature extraction space and proposes a dual-domain feature-enhanced network based on wavelet attention for SAR ship detection. Specifically, two wavelet attention modules are designed to independently and jointly compute attention for high-frequency and low-frequency features in the wavelet domain. Meanwhile, an embedding grouping strategy is adopted to reduce computational costs while enhancing the model’s detailed perception and global understanding of ship targets. Furthermore, a dynamic domain fusion (DDF) module is proposed to more effectively integrate wavelet-domain and spatial-domain information, enriching feature representation. Comprehensive experiments on two widely used SAR ship datasets demonstrate that the proposed method outperforms many other state-of-the-art detectors. The source code is available at https://github.com/Wenjing-Jiang-hbu/DFWA-Net