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Due to serious cloud contamination in optical satellite images, it is hard to acquire continuous cloud-free satellite observations, which limits the potential utilization of the available images and further data extraction and analysis. Thus, information reconstruction in cloud-contaminated images and the reprocessing of continuous cloud-free images are urgently needed for global change science. Many previous studies use one cloud-free reference image or multitemporal reference images to restore a target cloud-contaminated image; however, this paper is different and has developed a novel spatially and temporally weighted regression (STWR) model for cloud removal to produce continuous cloud-free Landsat images. The proposed method makes full utilization of cloud-free information from input Landsat scenes and employs a STWR model to optimally integrate complementary information from invariant similar pixels. Moreover, a prior modification term is added to minimize the biases derived from the spatially-weighted-regression-based prediction for each reference image. The results of the experimental tests with both simulated and actual Landsat series data show the proposed STWR can yield visually and quantitatively plausible recovery results. Compared with other cloud removal methods, our method produces lower biases and more robust efficacy. This approach provides a complete framework for continuous cloud removal and has the potential to be used for other optical images and to be applied to the reprocessing of cloud-free remote sensing productions.
Чэн et al. (Tue,) studied this question.