Abstract
With increasing processing power, deploying AI models for remote sensing directly onboard satellites is becoming feasible. However, new constraints arise, mainly when using raw, unprocessed sensor data instead of preprocessed ground-based products. While current solutions primarily rely on preprocessed sensor images, few approaches directly leverage raw data. This study investigates the effects of utilising raw data on deep learning models for object detection and classification tasks. We introduce a simulation workflow to generate raw-like products from high-resolution L1 imagery, enabling systemic evaluation. Two object detection models (YOLOv11s and YOLOX-S) are trained on both raw and L1 datasets, and their performance is compared using standard detection metrics and explainability tools. Results indicate that while both models perform similarly at low to medium confidence thresholds, the model trained on raw data struggles with object boundary identification at high confidence levels. It suggests that adapting AI architectures with improved contouring methods can enhance object detection on raw images, improving onboard AI for remote sensing.
Abstract (translated)
随着处理能力的提升，直接在卫星上部署用于遥感的人工智能模型变得越来越可行。然而，使用原始、未经处理的传感器数据而非预处理后的地面产品会带来新的限制和挑战。目前的解决方案主要依赖于预处理过的传感器图像，而直接利用原始数据的方法却很少见。这项研究探讨了使用原始数据对深度学习模型在目标检测和分类任务上的影响。我们引入了一种模拟工作流程，从高分辨率L1影像生成类似原始的产品，以实现系统的评估。两个目标检测模型（YOLOv11s 和 YOLOX-S）分别基于原始数据集和L1数据集进行训练，并通过标准的检测指标和解释工具对其性能进行了对比分析。 研究结果显示，在低至中等置信度阈值下，两种模型的表现相似；然而在高置信度水平时，基于原始数据训练的模型难以准确识别目标边界。这表明改进轮廓提取方法并适应AI架构可以提高对原始图像的目标检测能力，从而增强遥感任务中的在轨人工智能应用。
URL
https://arxiv.org/abs/2510.06858
