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

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...

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Veröffentlicht in:	ISPRS journal of photogrammetry and remote sensing Jg. 162; S. 148 - 160
Hauptverfasser:	Zhang, Qiang, Yuan, Qiangqiang, Li, Jie, Li, Zhiwei, Shen, Huanfeng, Zhang, Liangpei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.04.2020
Schlagworte:	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
ISSN:	0924-2716, 1872-8235
Online-Zugang:	Volltext
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