Earth Observation (EO) imagery is often degraded by atmospheric turbulence and pointing jitter; yet, these effects are rarely considered in datasets used to train AI-based detection models. Based on prior work, this paper presents an enhanced image simulator that enables the incorporation of vertical-path atmospheric turbulence and satellite pointing jitter, arising from platform and sensor vibrations, to generate physically realistic distorted images. As a case study, vessel detection is evaluated using YOLOv8 and RetinaNet on images generated by the proposed simulator under different levels of turbulence and pointing errors. Results show that YOLOv8 recall decreases from 91% under ideal conditions to 60% in the presence of weak turbulence, and falls below 40% under strong turbulence or jitter. In contrast, RetinaNet demonstrates greater robustness, maintaining approximately 75% recall across degraded conditions. These results highlight the importance of incorporating realistic physical degradations into EO training datasets to ensure reliable performance of AI-based models in operational environments, as demonstrated in maritime surveillance applications.

翻译：地球观测（EO）图像常因大气湍流和指向抖动而退化，然而，用于训练基于人工智能的检测模型的数据集却极少考虑这些效应。在先前工作的基础上，本文提出一种增强型图像模拟器，该模拟器能够整合垂直路径大气湍流以及由平台和传感器振动引起的卫星指向抖动，从而生成物理上逼真的失真图像。作为案例研究，本文利用YOLOv8和RetinaNet在所提出的模拟器在不同湍流和指向误差水平下生成的图像上评估了船只检测性能。结果表明，在理想条件下YOLOv8的召回率为91%，在弱湍流存在时下降至60%，而在强湍流或抖动条件下则低于40%。相比之下，RetinaNet表现出更强的鲁棒性，在退化条件下仍能保持约75%的召回率。这些结果凸显了将物理上真实的退化因素纳入地球观测训练数据集的重要性，以确保基于人工智能的模型在实际运行环境中具有可靠性能，这在海上监视应用中得到了实例证明。