Super-Resolution Land Cover Mapping Based on the Convolutional Neural Network

Super-Resolution Land Cover Mapping Based on the Convolutional Neural Network

Super-resolution mapping (SRM) is used to obtain fine-scale land cover maps from coarse remote sensing images. Spatial attraction, geostatistics, and using prior geographic information are conventional approaches used to derive fine-scale land cover maps. As the convolutional neural network (CNN) ha...

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Bibliographic Details
Main Authors: Yuanxin Jia, Yong Ge, Yuehong Chen, Sanping Li, Gerard B.M. Heuvelink, Feng Ling
Format: Article
Language:English
Published: MDPI AG 2019-08-01
Series:Remote Sensing
Subjects:
super-resolution mapping
land cover
convolutional neural network
remote sensing imagery
Online Access:https://www.mdpi.com/2072-4292/11/15/1815

Internet

https://www.mdpi.com/2072-4292/11/15/1815

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