FastVSDF: An Efficient Spatiotemporal Data Fusion Method for Seamless Data Cube

Spatiotemporal data fusion provides an efficacious strategy for addressing data gaps within time series datasets. This approach significantly enhances the feasibility of large-scale remote sensing applications by, for example, enabling the creation of seamless Data Cubes (SDC). Nevertheless, strict data input requirements and low computational efficiency of current methods severely limit the practicality of large-scale SDC production. In this study, we propose an efficient spatiotemporal data fusion method, the Fast Variation-based Spatiotemporal Data Fusion (FastVSDF) method. FastVSDF consists of 3 steps, i.e., unmixing, distributing global residuals, and distributing local residuals. In the unmixing process, FastVSDF introduces the fast abundant variation classification (FAVC) to mitigate sample imbalance and expedite the unsupervised classification. Then, the in-class Gaussian weight function is introduced to accelerate the distribution of local residuals by considering the classification to introduce the information on spectral similarity. Besides, FastVSDF employs Fast Guided Filter to combat the "block artifacts" of global residuals efficiently. Results show that FastVSDF demonstrated superior performance over Fit-FC, STARFM, RASDF, and FSDAF. More importantly, FastVSDF yields a remarkable improvement in computational efficiency, reducing predicting time by 43 to 573 times. As a practical application, we generated the Sentinel-2 SDC for the Yangtze River Basin, China. The fusion process for a single period's Yangtze River Basin dataset was accomplished within 20 minutes, with an average of 3.85 seconds for each Sentinel-2 scene. Comprehensively considering the efficiency, accuracy, feasibility, and universality, FastVSDF demonstrates the practical potential for constructing large-scale and long-term SDC. Our code will be publicly available at https://github.com/ChenXuAxel/FastVSDF.