Remote sensing image gap filling based on spatial-spectral random forests

Remote sensing images play a significant role in global land cover monitoring. However, due to the influence of cloud contamination, optical remote sensing images inevitably contain a large number of missing data, which severely limits their applicability. Existing cloud removal methods generally us...

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Détails bibliographiques
Auteurs principaux: Qunming Wang, Lanxing Wang, Xiaolin Zhu, Yong Ge, Xiaohua Tong, Peter M. Atkinson
Format: Artigo
Langue:Inglês
Publié: Elsevier 2022-06-01
Collection:Science of Remote Sensing
Sujets:
Cloud removal
Thick clouds
Random forests
Spectral information
Landsat 8
Sentinel-2
Accès en ligne:http://www.sciencedirect.com/science/article/pii/S2666017222000104
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