Learning-Based Superresolution Land Cover Mapping

Ling, Feng; Zhang, Yihang; Foody, Giles M.; Li, Xiaodong; Zhang, Xiuhua; Fang, Shiming; Li, Wenbo; Du, Yun

Abstract

Super-resolution mapping (SRM) is a technique for generating a fine spatial resolution land cover map from coarse spatial resolution fraction images estimated by soft classification. The prior model used to describe the fine spatial resolution land cover pattern is a key issue in SRM. Here, a novel learning based SRM algorithm, whose prior model is learned from other available fine spatial resolution land cover maps, is proposed. The approach is based on the assumption that the spatial arrangement of the land cover components for mixed pixel patches with similar fractions is often similar. The proposed SRM algorithm produces a learning database that includes a large number of patch pairs for which there is a fine and coarse spatial resolution representation for the same area. From the learning database, patch pairs that have similar coarse spatial resolution patches as those in input fraction images are selected. Fine spatial resolution patches in these selected patch pairs are then used to estimate the latent fine spatial resolution land cover map, by solving an optimization problem. The approach is illustrated by comparison against state-of-the-art SRM methods using land cover map subsets generated from the USA’s National Land Cover Database. Results show that the proposed SRM algorithm better maintains the spatial pattern of land covers for a range of different landscapes. The proposed SRM algorithm has the highest overall accuracy and Kappa values in all these SRM algorithms, by using the entire maps in the accuracy assessment.

Citation

Ling, F., Zhang, Y., Foody, G. M., Li, X., Zhang, X., Fang, S., Li, W., & Du, Y. (2016). Learning-Based Superresolution Land Cover Mapping. IEEE Transactions on Geoscience and Remote Sensing, 54(7), 3794-3810. https://doi.org/10.1109/TGRS.2016.2527841

• Journal Article Type: Article
• Acceptance Date: Jan 10, 2016
• Online Publication Date: Mar 15, 2016
• Publication Date: 2016-07
• Deposit Date: Apr 29, 2016
• Publicly Available Date: Apr 29, 2016
• Journal: IEEE Transactions on Geoscience and Remote Sensing
• Print ISSN: 0196-2892
• Electronic ISSN: 1558-0644
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 54
• Issue: 7
• Pages: 3794-3810
• DOI: https://doi.org/10.1109/TGRS.2016.2527841
• Keywords: Super-resolution mapping; learning database; patch pairs; neighboring patches
• Public URL: https://nottingham-repository.worktribe.com/output/791143
• Publisher URL: http://dx.doi.org/10.1109/TGRS.2016.2527841
• Additional Information: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.; ; Issue date: July 2016
• Contract Date: Apr 29, 2016