关键词: 卫星遥感, 目标检测, 复杂场景, 图像预处理, 强化学习, 自适应方法, 鲁棒性

Abstract: Satellite remote sensing image object detection constitutes a pivotal technique for earth observation and intelligent interpretation. However, most existing research has concentrated on ideal imaging conditions, and the capability for object detection under complex degradations, such as adverse weather, atmospheric turbulence, and noise interference, remains notably inadequate. To address this limitation, this paper proposes a reinforcement learning-based adaptive object detection methodology that dynamically orchestrates image preprocessing operators to achieve robust detection in complex scenarios. The core principle is to optimize object detection performance by leveraging reinforcement learning's decision-making capability to adaptively and iteratively select and compose preprocessing operations, including denoising, deblurring, and contrast enhancement, thereby enhancing both remote sensing imagery quality and detection precision. Experiments on the degraded scenarios constructed from the DIOR and DOTA satellite remote-sensing datasets with the YOLO11-OBB detector demonstrate that the proposed method achieves superior performance in all cases: on DIOR, the proposed method achieves mAP50 improvements of 11.1 and 2.5 points over Raw-Syn (trained on pristine data, validated on degraded data) and Syn-Syn (trained and validated on degraded data) baselines, respectively, attaining a final mAP50 of 80.8; on DOTA, it improves mAP50 by 7.2 and 2.8 points over the same baselines, reaching 76.6. Furthermore, the quality of processed remote sensing imagery is significantly enhanced (PSNR > 25 dB), substantiating the efficacy and applicability of the proposed approach in challenging environments.

Key words: Satellite remote sensing, Object detection, Complex scenarios, Image pre-processing, Reinforcement learning, Adaptive methods, Robustness