Weighted Spatiotemporal Fusion via Tensor Collaborative Representation

Spatiotemporal fusion of remote sensing data is one of the critical techniques for Earth's surface dynamic monitoring and analysis, which solves the limitation of spatial resolution and temporal coverage in individual sensor. In order to establish a more accurate and physically meaningful spatiotemporal fusion model, a weighted spatiotemporal fusion method via tensor collaborative representation (W-STFTCR) is proposed. Specifically, the collaborative representation (CR) constraint is incorporated into the tensor decomposition framework to prevent overfitting and enhance model robustness. Meanwhile, the superpixel segmentation strategy is adopted to partition the input difference image into superpixel blocks, facilitating block dictionary construction and clustering effectively. In addition, the normalized difference vegetation index (NDVI) and joint information entropy are introduced for weighting bands in predicting the final image, which leads to more accurate and physically meaningful outcomes. To verify the performance of the proposed method, the spatiotemporal fusion experiments on two publicly available datasets were conducted. The experiment results show that the proposed method outperforms the previous state-of-the-art (SOTA) spatiotemporal fusion algorithms, with excellent parameter robustness.