卫星遥感图像目标检测技术是当前对地观测与智能解译的重要手段。然而，现有研究多集中于理想成像条件下，对于复杂天气、大气扰动及噪声干扰等复杂因素影响下的目标检测能力仍显不足。针对这一问题，一种强化学习驱动的退化遥感图像目标检测方法被提出，其通过动态编排图像预处理算子以实现复杂场景下的鲁棒检测。该方法的核心思想是以目标检测性能为优化目标，利用强化学习的决策优势，自适应地迭代选择并组合图像去噪、去模糊、对比度增强等预处理操作，从而提升遥感影像的质量与检测精度。在使用YOLO11m-OBB检测器基于DIOR和DOTA卫星遥感数据集构建的退化场景上进行的实验表明，所提方法均取得优异表现：在DIOR数据集上，相较于Raw-Syn（原始数据训练、退化场景数据验证）和Syn-Syn（退化场景数据训练与验证）方案，mAP₅₀分别提升11.1%和2.5%，最终达80.8%；在DOTA数据集上，mAP₅₀较Raw-Syn和Syn-Syn分别提升7.2%和2.8%，最终达76.6%。同时，处理后遥感影像的图像质量明显提升（PSNR>25 dB），充分验证了所提方法在复杂环境下的有效性与适用性。

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 resulting detection performance remains notably insufficient under complex degradations， such as adverse weather， atmospheric turbulence， and noise interference. To address this limitation， a reinforcement learning-based adaptive object detection methodology is proposed for degraded remote sensing images. This methodology achieves robust detection in complex scenarios by dynamically orchestrating image preprocessing operators. 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 improving both remote sensing imagery quality and detection precision. Experiments on degraded scenarios constructed from the DIOR and DOTA satellite remote sensing datasets with the YOLO11m-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% over Raw-Syn （trained on pristine data， validated on degraded data） and Syn-Syn （trained and validated on degraded data） baselines， respectively， achieving a final mAP₅₀ of 80.8%. On DOTA， mAP₅₀ is improved by 7.2% and 2.8% 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.