Work

DA-GDNet: A Data-Augmented Gather-and-Distribute Network for Robust SAR Target Detection

Year: 2026

Type: article

Abstract: Synthetic Aperture Radar (SAR) possesses the capacity for all-weather imaging and is widely applied in target detection. However, robust SAR target detection remains challenging due to the limited availability of task-relevant labeled samples that jointly cover target categories, depression angles, and complex target–background contexts. In this paper, we propose a Data-Augmented Gather-and-Distribute Network (DA-GDNet) for SAR image target detection. By jointly optimizing at both the data and architectural levels, the proposed approach enhances the model’s capacity for target detection in complex backgrounds. Specifically, we design a SAR image data augmentation strategy that integrates three-dimensional modeling with deep learning. Meanwhile, we incorporate a Gather–Distribute (GD) mechanism and a Spatial Feature Enhancement Module (SFEM) to achieve efficient multi-scale feature fusion and enhance the saliency of target regions. Experimental results on the MSTAR dataset and ATRNet-STAR dataset demonstrate that DA-GDNet not only improves detection accuracy and robustness, but also significantly strengthens the model’s adaptability to variations in depression angles and complex backgrounds.

Source: Remote Sensing

DOI: 10.3390/rs18162839

Authors: Feihong Zhao, Yanfeng Li, Wenqian Wu, Houjin Chen, Yujing Shang

Institution: Beijing Jiaotong University

Language: English

Topic: Advanced SAR Imaging Techniques

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,258

Funder: National Natural Science Foundation of China