Clouds Classification from Sentinel-2 Imagery with Deep Residual Learning and Semantic Image Segmentation

Clouds Classification from Sentinel-2 Imagery with Deep Residual Learning and Semantic Image Segmentation

Detecting changes in land use and land cover (LULC) from space has long been the main goal of satellite remote sensing (RS), yet the existing and available algorithms for cloud classification are not reliable enough to attain this goal in an automated fashion. Clouds are very strong optical signals...

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Bibliographic Details
Main Authors: Cheng-Chien Liu, Yu-Cheng Zhang, Pei-Yin Chen, Chien-Chih Lai, Yi-Hsin Chen, Ji-Hong Cheng, Ming-Hsun Ko
Format: Article
Language:English
Published: MDPI AG 2019-01-01
Series:Remote Sensing
Subjects:
land use and land cover
change detection
cloud classification
deep learning
deep residual learning
semantic image segmentation
atrous convolution
Sentinel-2
CloudNet
Online Access:http://www.mdpi.com/2072-4292/11/2/119

Internet

http://www.mdpi.com/2072-4292/11/2/119

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