A Lightweight and Adaptive Image Inference Strategy for Earth Observation on LEO Satellites

Low Earth Orbit (LEO) satellite equipped with image inference capabilities (LEO-IISat) offer significant potential for Earth Observation (EO) missions. However, the dual challenges of limited computational capacity and unbalanced energy supply present significant obstacles. This paper introduces the Accuracy-Energy Efficiency (AEE) index to quantify inference accuracy unit of energy consumption and evaluate the inference performance of LEO-IISat. It also proposes a lightweight and adaptive image inference strategy utilizing the Markov Decision Process (MDP) and Deep Q Network (DQN), which dynamically optimizes model selection to balance accuracy and energy efficiency under varying conditions. Simulations demonstrate a 31.3% improvement in inference performance compared to a fixed model strategy at the same energy consumption, achieving a maximum inference accuracy of 91.8% and an average inference accuracy of 89.1%. Compared to MDP-Policy Gradient and MDP-Q Learning strategies, the proposed strategy improves the AEE by 12.2% and 6.09%, respectively.