Satellite Video Intrinsic Decomposition
Guoming Gao, Yanfeng Gu, Shengyang Li · IEEE Transactions on Geoscience and Remote Sensing · 2022
Existing satellite video processing methods are mainly based on original video, ignoring the use of invariant background characteristics of staring satellites, and easy to be disturbed by rapid light changes. In order to improve application capability of satellite video, this paper establishes the satellite video intrinsic decomposition (SVID) model, including satellite video signal composition model, decomposition constraint with time-spatial unity similarity constraint, static and dynamic components separation by improving TRPCA, and decomposition acceleration based on reflectance transfer. With SVID, intrinsic decomposition and dynamic and static component separation are realized. Five Jilin-1 satellite videos are used to verify the validity, superiority and the potential applications of the proposed algorithm. By comparing with state-of-the-art intrinsic image decomposition method and intrinsic video decomposition method, the experimental results prove the superiority of the SVID method in extracting reflectance component. In addition, the experimental results also prove SVID has excellent application ability in scene background analysis and moving target tracking.