s play a crucial role in remote sensing applications, particularly in the transmission of remote sensing images. However, the occurrence of poses significant challenges in meeting the increased bandwidth demands. Traditional networks are ill-equipped to handle such bursts due to their pre-deployed content. In this paper, we propose an optimal replication strategy for mitigating in s, specifically focusing on the transmission of remote sensing images. Our strategy involves selecting the most optimal replication delivery satellite node when multiple users subscribe to the same remote sensing content within a short time, effectively reducing network transmission data and preventing throughput degradation caused by expansion. We formulate the process as a multi-objective optimization problem and apply Markov decision processes to determine the optimal value for reduction. To address these challenges, we leverage techniques. Additionally, we use s with subdivision and data identification methods to enable rapid retrieval and encoding of remote sensing images. Through software-based simulations using a low Earth orbit satellite constellation, we validate the effectiveness of our proposed strategy, achieving a significant 17% reduction in the average delivery delay. This paper offers valuable insights into efficient in satellite networks, specifically targeting the transmission of remote sensing images, and presents a promising approach to mitigate challenges in information-centric environments.