A Flexible Object-Level Processing Strategy to Enhance the Weight Function-Based Spatiotemporal Fusion Method

Spatiotemporal fusion technique provides a cost-efficient way to achieve dense time series observation. Among all categories of spatiotemporal fusion methods, the weight function-based method attracted considerable attention. However, this kind of method selects similar pixels in a regular window without considering the distribution of features, which will weaken its ability to preserve the structure information. Besides, the weight function-based method carries out pixel-by-pixel fusion computation, which leads to computational inefficiency. To solve the aforementioned issues, a flexible object-level processing strategy is proposed in this paper. Three popular spatiotemporal fusion methods include the spatial and temporal adaptive reflectance fusion model (STARFM), the enhance STARFM (ESTARFM) and the three-step method (Fit-FC) were selected as examples to analyze and validate the effectiveness of object-level processing strategy. Four study sites with different surface landscapes and change patterns were adopted for experiments. Experimental results indicated that the object-level fusion versions of STARFM, ESTARFM, Fit-FC can better preserve the structural information, and were 102.89-113.71, 92.77-115.73, 30.51-36.15 times faster than their original methods. Remarkably, the object-level fusion versions of Fit-FC outperforms all competing methods in one-pair case fusion experiments, especially in PY area (RMSE is 0.0343 vs. 0.0380, r is 0.7469 vs. 0.6986 compare with Fit-FC). Additionally, the object-level processing strategy can also be adopted to enhance other methods which uses the principle of combining similar adjacent information. The program and test data available at https://github.com/Andy-cumt.