Adaptive Method for Landsat ETM+ Gap Filling Using Successive Temporal Images

Systematic fact gaps on retrieved imagery were imposed by failure of the (SLC) on ETM+ leading to elimination of potential to provide spatially continuous fields. While a number of algorithms were developed for filling these gaps, the majority of the suggested algorithms can only be applied on fairly homogeneous areas. The retrieving band aspect and element may be challenging if they are utilized to heterogeneous landscapes. In this study, we adopted the criterion of correlation between satellite images to determine spatially match between images. Three different multi temporal images were used in filling the gaps in a period not exceeding 48 days, and two algorithms are presented. In the first algorithm, we got one image free of gaps, when using three images of different times, for a period not exceeding 48 days, while in the second algorithm, we got images free of gaps within a period of time not exceeding 16 days depending on the images resulting from the first algorithm. Simulated and true SLC-off ETM+ bands have been used to examine the performance of the proposed method via comparing with the original data so as to examine the resulting bands from the first and the second algorithms which are based on the correlation model. The statistical reviews indicate that in the proposed methods we can get better values of un-scanned pixels accurately, specifically in target images (vegetation, soil and water).