Unsupervised Satellite Video Deep Intrinsic Decomposition Using Physical Prior Constraints

Abstract

Satellite video intrinsic decomposition has emerged as a promising area of research with significant application potential. However, existing methods still have certain limitations that hinder their effectiveness in extracting high-quality intrinsic information from complex scenes, ensuring temporal stability of the reflectance component, and achieving computational efficiency. In this article, an unsupervised satellite video intrinsic decomposition network (USVIDNet) is proposed, which overcomes the limitations encountered by existing methods. The USVIDNet incorporates three loss functions based on physical priors: reconstruction loss, chromaticity consistency loss, and spatiotemporal reflectance similarity loss, which provide constraints to guide the intrinsic decomposition process, eliminating the dependence on ground truth intrinsic images required for supervised learning. The network is based on a U-Net architecture variant, consisting of an encoder and two decoders. The encoder captures essential features of the input satellite video, while the decoders focus on predicting two components: reflectance and shading. To enhance the processing efficiency of intrinsic decomposition, a novel initialization-decomposition mode is proposed by leveraging the invariant background characteristics of staring satellites. Experiments are conducted on six Jilin-1 satellite videos to assess the performance of the proposed method in terms of intrinsic component extraction and improved ability of satellite video applications. The experimental results demonstrate the superiority of the proposed method.