Application of Video Super-Resolution Reconstruction Algorithm for Luojia3-01 Satellite

Super-resolution reconstruction for optical remote sensing images enhances the spatial resolution of satellite imagery and video, improving detection and identification accuracy. This technology is crucial in fields such as public service, disaster response, national defense, and military applications. Dynamic target detection, particularly in intelligent transportation and military remote sensing, has attracted significant attention. Extracting motion information from dynamic targets aids in studying their behavior and enables tracking and trajectory prediction. However, factors such as environmental conditions and hardware limitations often degrade the quality of remote sensing video, leading to loss of detail in dynamic targets. Restoring high-resolution dynamic targets from satellite video sequences remains a challenge. In this article, we use satellite video data from the Luojia3-01 satellite to address this issue. First, we construct a super-resolution reconstruction dataset from preprocessed satellite video, containing multiple dynamic targets. We then propose a new video super-resolution network, ETDM_Plus, which incorporates a dynamic fusion attention module to enhance resolution by focusing on dynamic targets and utilizing spatiotemporal information. Experimental results show that in the dynamic target area, the proposed method improves the peak signal-to-noise ratio by 2.28–3.85% and structural similarity by 0.94–3.32%, compared with the second-best method. The Luojia_VSRSet dataset facilitates further research in this domain. The proposed network generates high-quality artifact-free high-resolution frames across all dynamic target scenes, enhancing the use of Luojia3-01 satellite video for related applications.