Abstract:

The ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) is a classic spatiotemporal filter-based algorithm, which is used in the many fields. The similar pixel selection process in the ESTARFM model is affected by the size of the size of search window and the number of classifications. In the current study, the size of the search windows is more uniform, and the number of classifications lacks uniformity. In order to reduce the influence of the number of classifications in the ESTARFM algorithm on the performance of the algorithm. The similar pixel selection method in the STNLFFM (A Spatial and Temporal Nonlocal Filter-Based Data Fusion Method) combined with the ESTARFM model to propose the ESTARFM_NL model. The study designed two sets of data under different conditions of phase change for comparative analysis. The results show that the overall distribution of the relative error histogram of ESTARFM_NL and ESTARFM is tight and consistent. When the fusion results are evaluated by the average relative error and correlation coefficient, the difference between the two algorithms is considerable, indicating that the fusion accuracy of the two algorithms is equivalent. Comparing the efficiency of the two algorithms, we found that the ESTARFM_NL running time can be greatly reduced. Therefore, ESTARFM_NL provides an alternative fusion scheme for large-area or long-term sequence remote sensing data with large data volume.

Key words: Spatiotemporal fusion, ESTARFM, Similar pixel selection, Threshold value method, Running efficiency

鎽樿锛�

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鍏抽敭璇�: 鏃剁┖铻嶅悎, ESTARFM, 鐩镐技鍍忓厓閫夊彇, 闃堝�兼硶, 杩愯鏁堢巼