发表机构
IRT Saint Exupéry（圣埃克苏佩里研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对地球观测卫星数据过载问题，在Versal嵌入式硬件上部署YOLOX-S船舶检测器，提出多种下传模式实现数据缩减，在港口和沿海场景中取得良好的保留船舶与数据体积的权衡效果。
AI 中文摘要
超高分辨率地球观测卫星采集的数据超出其存储和下传能力，而海上监视中船舶仅占每幅场景的极小部分。本研究将星载船舶检测作为选择下传数据的方式，可根据内容缩减数据，而非对每个像素编码，这与传统星载压缩互为补充。研究围绕三个方向展开：（i）数据与算法：从68幅标注的Maxar场景生成受控数据集，包含43类船舶，并在该数据集上训练YOLOX-S检测器；（ii）嵌入式部署：将该检测器量化后部署在Versal VC1902的DPU上，检测质量损失有限，每幅场景处理时间为几秒；（iii）数据缩减：提出多种下传模式，从仅元数据（每幅检测的边界框、类别和置信度）到船舶周围图像裁剪块、包含检测的图块，或背景降级的整幅场景，并根据实测检测误差估算每种模式在保留船舶数量与下传体积间的权衡。在密集港口和沿海场景中，图块保留98%的船舶，仅占场景体积的29%；裁剪块保留83%的船舶，仅占3%。
英文摘要
Very-high-resolution Earth-observation satellites acquire more data than they can store and downlink, while in maritime surveillance the vessels cover a tiny fraction of each scene. We study onboard vessel detection as a way to select what is downlinked, which reduces the data according to its content rather than coding every pixel; it is complementary to conventional onboard compression. The work follows three axes. (i) Data and algorithm: a controlled dataset is generated from 68 annotated Maxar scenes with 43 vessel classes, and a YOLOX-S detector is trained on it. (ii) Embedded deployment: the detector is quantized and deployed on the DPU of a Versal VC1902, with a limited loss of detection quality and a processing time of a few seconds per scene. (iii) Data reduction: we propose several downlink modes, from metadata only (box, class and score of each detection) to image crops around vessels, tiles holding detections, or the whole scene with a degraded background, and estimate from the measured detection errors the trade-off each offers between the vessels kept and the volume downlinked. On our dense harbor and coastal scenes, tiles keep 98% of the vessels with 29% of the scene volume, and crops 83% with 3%.
Comments8 pages. Accepted at the 10th On-Board Payload Data Compression Workshop (OBPDC 2026), Barcelona, Spain, 12-14 October 2026
