用模拟原始数据提升卫星上AI目标检测能力
Explaining raw data complexity to improve satellite onboard processing
- 构建仿真流程生成类原始数据用于评估
- 原始数据训练模型在高置信度下边界识别差
- 适合关注星载AI优化与遥感图像处理的科研者
随着处理能力提升，将AI模型直接部署于卫星上进行遥感处理已成可能。然而，使用未经处理的原始传感器数据而非预处理的地基产品会带来新挑战。当前多数方法依赖预处理图像，直接利用原始数据的研究较少。本研究系统评估了原始数据对目标检测与分类任务中深度学习模型的影响。通过从高分辨率L1影像生成类原始产品，构建仿真工作流以实现系统性评测。在原始数据与L1数据集上分别训练YOLOv11n和YOLOX-S两个目标检测模型，并使用标准检测指标与可解释性工具对比性能。结果显示，在低至中等置信度下两模型表现相近，但在高置信度下原始数据训练模型在物体边界识别上表现明显下降。这表明需改进模型轮廓提取能力，以提升原始图像上的目标检测效果，从而增强星载遥感AI的实用性。
原文摘要 · Abstract (English)
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 (YOLOv11n 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.
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