Zhuoyun Du, LujieZheng LujieZheng, Renjun Hu, Yuyang Xu, Xiawei Li, Ying Sun, Wei Chen, Jian Wu, Haolei Cai, Haochao Ying. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

With the enormous scale of remote sensing imagery generation, on-orbit computing has become a crucial paradigm to enable near-real-time processing. Due to the limited onboard resources and on-orbit power supply, satellite edge computing (SEC) is developed for satellite-ground collaboration, aiding on-orbit computation. However, the intermittent satellite-to-ground transmission link poses an efficiency challenge when collaborating SEC resources. Therefore, this article proposes a satellite edge computing resource scheduling technique for on-orbit remote sensing imagery processing ($\textsf {SECORS}$). First, we design a remote sensing mission-specific SEC architecture, which involves an offline-online satellite working mode. Subsequently, a computational resource scheduling model ($\textsf {SEC}$-$\textsf {RSM}$) is established, including the directed acyclic graph (DAG) model and mathematical problem formulation. Next, to obtain effective scheduling solutions, we develop an end-to-end algorithm leveraging the multiagent proximal policy optimization and heuristic rule of the earliest finish time ($\textsf {SEC}$-$\textsf {MPH}$). Finally, we build a simulation SEC platform to carry out experiments and implement several methods as the comparison including multiobjective evolutionary algorithms, deep reinforcement learning approaches, and the scheme without optimization (baseline). Simulation results show that$\textsf {SECORS}$achieves 68.87% and 66.60% reductions in time and energy for on-orbit computation. Moreover, our method improves the energy efficiency ratio (EER) by three times and achieves high processing capacity with 548 pixels per unit of power (W) and time (ms).