Nanosatellite constellations equipped with sensors capturing large geographic regions provide unprecedented opportunities for Earth observation. As constellation sizes increase, network contention poses a downlink bottleneck. Orbital Edge Computing (OEC) leverages limited onboard compute resources to reduce transfer costs by processing the raw captures at the source. However, current solutions have limited practicability due to reliance on crude filtering methods or over-prioritizing particular downstream tasks. This work presents FOOL, an OEC-native and task-agnostic feature compression method that preserves prediction performance. FOOL partitions high-resolution satellite imagery to maximize throughput. Further, it embeds context and leverages inter-tile dependencies to lower transfer costs with negligible overhead. While FOOL is a feature compressor, it can recover images with competitive scores on perceptual quality measures at lower bitrates. We extensively evaluate transfer cost reduction by including the peculiarity of intermittently available network connections in low earth orbit. Lastly, we test the feasibility of our system for standardized nanosatellite form factors. We demonstrate that FOOL permits downlinking over 100x the data volume without relying on prior information on the downstream tasks.

翻译：搭载大范围地理区域感知传感器的纳米卫星星座为地球观测提供了前所未有的机遇。随着星座规模扩大，网络争用引发了下行链路瓶颈。轨道边缘计算（OEC）利用有限的星载计算资源，通过在数据源头处理原始捕获信息来降低传输成本。然而，现有方案因依赖粗粒度过滤方法或过度偏重特定下游任务而实用性有限。本文提出FOOL——一种原生适配OEC且任务无关的特征压缩方法，可在保持预测性能的前提下实现数据压缩。FOOL通过划分高分辨率卫星图像以最大化吞吐量，同时嵌入上下文信息并利用分块间依赖关系，以极小计算开销降低传输成本。尽管FOOL本质为特征压缩器，但其能以更低码率恢复出在感知质量指标上具有竞争力的图像。我们通过纳入低轨网络间歇性连通的特殊性，全面评估了传输成本降低效果。最后，针对标准化纳米卫星平台规格验证了系统的可行性。实验表明，FOOL可在不依赖下游任务先验信息的情况下，实现超过100倍的数据下传量提升。