雷达学报2022，Vol.11Issue(5)：884-896,13.DOI:10.12000/JR22121
结合强化学习自适应候选框挑选的SAR目标检测方法
Adaptive Region Proposal Selection for SAR Target Detection Using Reinforcement Learning
摘要
Abstract
Compared with optical images, the background clutter has a greater impact on feature extraction in Synthetic Aperture Radar (SAR) images. Due to the traditional redundant region proposals on the entire feature map, these algorithms generate large quantities of false alarms under the influence of clutter in SAR images, thereby lowering the target detection accuracy. To address this issue, this study proposes a Faster R-CNN model-based SAR target detection method, which uses reinforcement learning to realize adaptive region proposal selection. This method can adaptively locate areas that may contain targets on the feature map using the sequential decision-making characteristic of reinforcement learning and simultaneously adjust the scope of the next search area according to previous search results using distance constraints in reinforcement learning. Thus, this method can reduce the impact of complex background clutter and the computation of reinforcement learning. The experimental results based on the measured data indicate that the proposed method improves the detection performance.关键词
合成孔径雷达/目标检测/强化学习/Faster/R-CNN算法Key words
Synthetic Aperture Radar (SAR)/Target detection/Reinforcement learning/Faster R-CNN分类
信息技术与安全科学引用本文复制引用
杜兰,王梓霖,郭昱辰,杜宇昂,严俊坤..结合强化学习自适应候选框挑选的SAR目标检测方法 [J].雷达学报,2022,11(5):884-896,13.基金项目
国家自然科学基金(U21B2039) （U21B2039）
