Deriving Non-Cloud Contaminated Sentinel-2 Images with RGB and Near-Infrared Bands from Sentinel-1 Images Based on a Conditional Generative Adversarial Network

Deriving Non-Cloud Contaminated Sentinel-2 Images with RGB and Near-Infrared Bands from Sentinel-1 Images Based on a Conditional Generative Adversarial Network

Sentinel-2 images have been widely used in studying land surface phenomena and processes, but they inevitably suffer from cloud contamination. To solve this critical optical data availability issue, it is ideal to fuse Sentinel-1 and Sentinel-2 images to create fused, cloud-free Sentinel-2-like imag...

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Bibliographic Details
Main Authors: Quan Xiong, Liping Di, Quanlong Feng, Diyou Liu, Wei Liu, Xuli Zan, Lin Zhang, Dehai Zhu, Zhe Liu, Xiaochuang Yao, Xiaodong Zhang
Format: Article
Language:English
Published: MDPI AG 2021-04-01
Series:Remote Sensing
Subjects:
Sentinel-1
Sentinel-2
generative adversarial network
non-cloud contamination
data fusion
Online Access:https://www.mdpi.com/2072-4292/13/8/1512

Internet

https://www.mdpi.com/2072-4292/13/8/1512

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