Super-resolution land cover mapping by deep learning

Ling, Feng; Foody, Giles M.

Abstract

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.

Citation

Ling, F., & Foody, G. M. (2019). Super-resolution land cover mapping by deep learning. Remote Sensing Letters, 10(6), 598-606. https://doi.org/10.1080/2150704x.2019.1587196

• 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
• DOI: https://doi.org/10.1080/2150704x.2019.1587196
• Keywords: Deep learning; Convolutional neural network; Super-resolution
• Public URL: https://nottingham-repository.worktribe.com/output/1615937
• Publisher URL: https://www.tandfonline.com/doi/full/10.1080/2150704X.2019.1587196
• Additional Information: This is an Accepted Manuscript of an article published by Taylor & Francis in Remote Sensing Letters on 01/03/2019, available online: http://www.tandfonline.com/10.1080/2150704x.2019.1587196