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Super-resolution land cover mapping by deep learning

Ling, Feng; Foody, Giles M.

Super-resolution land cover mapping by deep learning Thumbnail


Feng Ling

Professor of Geographical Information


Super-resolution mapping (SRM) is a technique to estimate a fine spatial resolution land cover map from coarse spatial resolution fractional proportion images. SRM is often based explicitly on the use of a spatial pattern model that represents the land cover mosaic at the fine spatial resolution. Recently developed deep learning methods have considerable potential as an alternative approach for SRM, based on learning the spatial pattern of land cover from existing fine resolution data such as land cover maps. This letter proposes a deep learning-based SRM algorithm (DeepSRM). A deep convolutional neural network was first trained to estimate a fine resolution indicator image for each class from the coarse resolution fractional image, and all indicator maps were then combined to create the final fine resolution land cover map based on the maximal value strategy. The results of an experiment undertaken with simulated images show that DeepSRM was superior to conventional hard classification and a suite of popular SRM algorithms, yielding the most accurate land cover representation. Consequently, methods such as DeepSRM may help exploit the potential of remote sensing as a source of accurate land cover information.


Ling, F., & Foody, G. M. (2019). Super-resolution land cover mapping by deep learning. Remote Sensing Letters, 10(6), 598-606.

Journal Article Type Article
Acceptance Date Feb 16, 2019
Online Publication Date Mar 1, 2019
Publication Date Jun 3, 2019
Deposit Date Mar 7, 2019
Publicly Available Date Mar 2, 2020
Journal Remote Sensing Letters
Print ISSN 2150-704X
Electronic ISSN 2150-7058
Publisher Taylor and Francis
Peer Reviewed Peer Reviewed
Volume 10
Issue 6
Pages 598-606
Keywords Deep learning; Convolutional neural network; Super-resolution
Public URL
Publisher URL
Additional Information This is an Accepted Manuscript of an article published by Taylor & Francis in Remote Sensing Letters on 01/03/2019, available online:


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