Impact of Atmospheric Turbulence and Pointing Error on Earth Observation

TL;DR

This paper introduces an enhanced image simulator that models atmospheric turbulence and pointing jitter to generate realistic degraded Earth Observation images, evaluating their impact on vessel detection accuracy using AI models.

Contribution

The paper presents a novel physically realistic image simulator incorporating atmospheric turbulence and pointing jitter for EO data augmentation.

Findings

YOLOv8 recall drops from 91% to below 40% under turbulence and jitter.

RetinaNet maintains around 75% recall despite image degradations.

Simulating physical degradations improves AI model robustness in EO applications.

Abstract

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.