In real-time and high-resolution Earth observation imagery, Low Earth Orbit (LEO) satellites capture images that are subsequently transmitted to ground to create an updated map of an area of interest. Such maps provide valuable information for meteorology or environmental monitoring, but can also be employed in near-real time operation for disaster detection, identification, and management. However, the amount of data generated by these applications can easily exceed the communication capabilities of LEO satellites, leading to congestion and packet dropping. To avoid these problems, the Inter-Satellite Links (ISLs) can be used to distribute the data among the satellites for processing. In this paper, we address an energy minimization problem based on a general satellite mobile edge computing (SMEC) framework for real-time and very-high resolution Earth observation. Our results illustrate that the optimal allocation of data and selection of the compression parameters increase the amount of images that the system can support by a factor of 12 when compared to directly downloading the data. Further, energy savings greater than 11% were observed in a real-life scenario of imaging a volcanic island, while a sensitivity analysis of the image acquisition process demonstrates that potential energy savings can be as high as 92%.

翻译：在实时高分辨率对地观测影像中，低地球轨道（LEO）卫星捕获图像后将其传输至地面，以生成感兴趣区域的最新地图。这类地图可为气象学或环境监测提供宝贵信息，同时也可用于近实时操作的灾害检测、识别与管理。然而，这些应用产生的数据量极易超出LEO卫星的通信能力，导致网络拥塞和数据包丢失。为避免上述问题，可利用星间链路（ISLs）在卫星间分发数据以进行处理。本文基于通用卫星移动边缘计算（SMEC）框架，针对实时与甚高分辨率对地观测中的能量最小化问题展开研究。结果表明，相较于直接下载数据，通过优化数据分配与压缩参数选择，系统可支持的图像数量提升12倍。此外，在火山岛成像的真实场景中，观察到超过11%的节能效果；而图像采集过程的敏感性分析显示，潜在节能效果最高可达92%。