  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%.
