在遥感目标检测方面，特别是对车辆、飞机等遥感目标的检测，在交通监测、军事侦察等方面具有重要的应用价值，对相关技术的进步也起到了积极的推动作用。然而，遥感目标检测仍面临诸多挑战，包括遥感图像标注成本高，以及复杂天气条件对目标检测性能的干扰。为解决上述问题，本文提出了一种融合强化学习与可控图像渲染的遥感图像合成方法，将虚实混合的遥感图像生成任务转化为基于强化学习的场景参数搜索问题。与此同时，本文引入天气干扰因素，通过模拟云层、沙尘和雾气等复杂天气条件对训练数据进行增强，提升数据在复杂环境下的适应性与真实性。实验结果表明，相较于传统方法，本文方法在多项评价指标上均取得显著提升，最高性能提升达4.4%。这验证了本文方法的有效性，显示了合成遥感图像及其复杂天气效果在提升目标检测模型精度方面的潜力，为遥感目标检测技术的发展提供了有力支持。本文部分代码已开源，项目地址为：https://github.com/fantq1005/RL-SynthRS。

In the field of remote sensing target detection, particularly for vehicles, aircraft, and other targets, the technology plays a crucial role in applications such as traffic monitoring and military reconnaissance, significantly promoting advancements in related techniques. However, remote sensing target detection still faces numerous challenges, including the high cost of image annotation and the interference of complex weather conditions on detection per-formance. To address these issues, this paper proposes a novel image synthesis method that integrates reinforce-ment learning with controllable image rendering, transforming the generation of mixed real and synthetic remote sensing data into a reinforcement learning-based scene parameter search problem. At the same time, weather dis-turbance factors are introduced by simulating clouds, dust, and fog to augment the training data, enhancing its adaptability and realism in complex environments. Experimental results show that, compared with traditional meth-ods, the proposed method achieves significant improvements across multiple evaluation metrics, with a maximum performance gain of 4.4%. This verifies the effectiveness of the proposed approach and highlights the potential of synthetic remote sensing images and complex weather effects in improving target detection model accuracy, there-by providing strong support for the advancement of remote sensing target detection technology. The code for this work has been partially open-sourced and is available at: https://github.com/fantq1005/RL-SynthRS.