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 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：配备传感器并能够捕获广阔地理区域的纳卫星星座为地球观测提供了前所未有的机遇。随着星座规模的扩大，网络竞争导致了下行链路瓶颈。轨道边缘计算利用有限的车载计算资源，通过在源头处理原始捕获数据来降低传输成本。然而，现有解决方案因依赖粗糙的过滤方法或过度优先处理特定下游任务而实用性有限。本研究提出了FOOL，一种轨道边缘计算原生且与任务无关的特征压缩方法，能够保持预测性能。FOOL通过对高分辨率卫星图像进行分区处理以最大化吞吐量。此外，它通过嵌入上下文信息并利用图块间的依赖关系，以可忽略的开销降低传输成本。尽管FOOL是一种特征压缩器，但它能够在较低比特率下重建图像，并在质量评估指标上获得具有竞争力的分数。我们通过纳入低地球轨道中间歇可用网络连接的特殊性，对传输成本降低效果进行了全面评估。最后，我们测试了该系统在标准化纳卫星形态因子上的可行性。实验证明，FOOL无需依赖下游任务的先验信息即可实现超过100倍的数据量下行传输。