数据采集与处理2025，Vol.40Issue(3)：699-710,12.DOI:10.16337/j.1004-9037.2025.03.011
基于边缘特征引导学习的SAR目标检测
SAR Target Detection Based on Edge Feature Guided Learning
摘要
Abstract
Synthetic aperture radar(SAR)image targets typically exhibit subtle edge features,which can vary across different scales.Edge features provide crucial information about the shape and contour of target objects,improving the model's localization capabilities.However,existing SAR object detection methods often underperform in learning edge features,limiting their ability to accurately perceive target edges.To address this,we propose a SAR target detection method based on edge feature guided learning(EFGL).This approach builds upon the fully convolutional one-stage(FCOS)object detection framework and leverages edge features to guide the learning process in feature pyramid networks(FPN).By integrating an edge operator module into FPN,the network's capacity to learn multi-scale edge features is explicitly enhanced.Additionally,during multi-scale feature fusion,we introduce an edge feature-guided fusion module that incorporates a spatial attention mechanism to enable edge-guided fusion across adjacent feature levels.On the MSAR and SAR-Aircraft-1.0 datasets,the proposed method achieves detection accuracies of 68.68%and 67.44%under the AP'07 standard,showing improvements of 1.34%and 4.81%over the baseline network,respectively compared to other related algorithms,this method demonstrates superior target localization and overall performance in SAR target detection.关键词
合成孔径雷达/目标检测/深度学习/注意力机制/特征融合Key words
synthetic aperture radar(SAR)/target detection/deep learning/attention mechanism/feature fusion分类
信息技术与安全科学引用本文复制引用
倪康,孙笠焜,邹旻瑞..基于边缘特征引导学习的SAR目标检测[J].数据采集与处理,2025,40(3):699-710,12.基金项目
国家自然科学基金(62101280) （62101280）
江苏省自然科学基金(BK20210588) （BK20210588）
中国博士后科学基金(2023M731781) （2023M731781）
江苏省航空对地探测与智能感知工程中心开放基金(JSECF2023-05) （JSECF2023-05）
雷达成像与微波光子技术教育部重点实验室(南京航空航天大学)基金(NJ20230005). （南京航空航天大学）
