Thick cloud and cloud shadow removal in multitemporal imagery using progressively spatio-temporal patch group deep learning

• Published in: ISPRS journal of photogrammetry and remote sensing Vol. 162; pp. 148 - 160
• Main Authors: Zhang, Qiang, Yuan, Qiangqiang, Li, Jie, Li, Zhiwei, Shen, Huanfeng, Zhang, Liangpei
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.04.2020
• Subjects: cloud cover; Gap-filling; geospatial data processing; Global-local CNN; image analysis; Landsat; Patch group; Progressive iteration; remote sensing; spatial data; Spatio-temporal; Thick cloud and cloud shadow; Spatio-temporal; Patch group; Global-local CNN; Progressive iteration; Thick cloud and cloud shadow; Gap-filling

Thick cloud and its shadow severely reduce the data usability of optical satellite remote sensing data. Although many approaches have been presented for cloud and cloud shadow removal, most of these approaches are still inadequate in terms of dealing with the following three issues: (1) thick cloud...