A spatiotemporal fusion method based on interpretable deep networks.

Remote sensing spatiotemporal fusion is currently a popular research field in remote sensing that can cost-effectively generate remote sensing images with high spatiotemporal resolution. The deep neural network-based approach has strong feature extraction capability and has achieved great success in the fields of signal and image processing, but the deep network lacks interpretability. In this paper, a new interpretable deep network-based spatiotemporal fusion (UNSTF) method is proposed. The method proposes a new network model by first establishing a concise a priori formulation using sparse representation, constructing the proposed network by unfolding the proximal gradient algorithm for solving the model, and carefully designing each basic network module in the model to have a reasonable physical meaning, making the whole network interpretable. In addition, the UNSTF network method proposes a new network iteration loss function, where the predicted images of each iteration stage of the network are constrained by the real images, and the final stage and the intermediate network stages conform well to their inherent prior structures, effectively improving the accuracy and reliability of model prediction. Through extensive experiments, it is shown that the proposed method outperforms existing fusion methods in terms of both subjective and objective metrics. [ABSTRACT FROM AUTHOR]