Spatially and Temporally Weighted Regression: A Novel Method to Produce Continuous Cloud-Free Landsat Imagery
作者:Chen, B (Chen, Bin)[ 1 ] ; Huang, B (Huang, Bo)[ 2 ] ; Chen, LF (Chen, Lifan)[ 1,3 ] ; Xu, B (Xu, Bing)[ 1,4,5 ]
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷: 55 期: 1 页: 27-37
出版年: JAN 2017
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.
通讯作者地址: Chen, B (通讯作者)
Beijing Normal Univ, State Key Lab Remote Sensing Sci, Coll Global Change & Earth Syst Sci, Beijing 100875, Peoples R China.
[ 1 ] Beijing Normal Univ, State Key Lab Remote Sensing Sci, Coll Global Change & Earth Syst Sci, Beijing 100875, Peoples R China
[ 2 ] Chinese Univ Hong Kong, Dept Geog & Resource Management, Shatin, Hong Kong, Peoples R China
[ 3 ] China Meteorol Adm, Natl Meteorol Informat Ctr, Beijing 100081, Peoples R China
[ 4 ] Tsinghua Univ, Minist Educ, Key Lab Earth Syst Modeling, Ctr Earth Syst Sci, Beijing 100084, Peoples R China
[ 5 ] Univ Utah, Dept Geog, Salt Lake City, UT 84112 USA