Spatial-temporal super-resolution land cover mapping with a local spatial-temporal dependence model

Li, Xiaodong; Ling, Feng; Foody, Giles M.; Ge, Yong; Zhang, Yihang; Wang, Lihui; Shi, Lingfei; Li, Xinyan; Du, Yun

Abstract

The mixed pixel problem is common in remote sensing. A soft classification can generate land cover class fraction images that illustrate the areal proportions of the various land cover classes within pixels. The spatial distribution of land cover classes within each mixed pixel is, however, not represented. Super-resolution land cover mapping (SRM) is a technique to predict the spatial distribution of land cover classes within the mixed pixel using fraction images as input. Spatial-temporal SRM (STSRM) extends the basic SRM to include a temporal dimension by using a finer-spatial resolution land cover map that pre-or postdates the image acquisition time as ancillary data. Traditional STSRM methods often use one land cover map as the constraint, but neglect the majority of available land cover maps acquired at different dates and of the same scene in reconstructing a full state trajectory of land cover changes when applying STSRM to time series data. In addition, the STSRM methods define the temporal dependence globally, and neglect the spatial variation of land cover temporal dependence intensity within images. A novel local STSRM (LSTSRM) is proposed in this paper. LSTSRM incorporates more than one available land cover map to constrain the solution, and develops a local temporal dependence model, in which the temporal dependence intensity may vary spatially. The results show that LSTSRM can eliminate speckle-like artifacts and reconstruct the spatial patterns of land cover patches in the resulting maps, and increase the overall accuracy compared with other STSRM methods.

Citation

Li, X., Ling, F., Foody, G. M., Ge, Y., Zhang, Y., Wang, L., …Du, Y. (2019). Spatial-temporal super-resolution land cover mapping with a local spatial-temporal dependence model. IEEE Transactions on Geoscience and Remote Sensing, 57(7), 4951-4966. https://doi.org/10.1109/TGRS.2019.2894773

• Journal Article Type: Article
• Acceptance Date: Dec 14, 2018
• Online Publication Date: Mar 29, 2019
• Publication Date: Jul 1, 2019
• Deposit Date: Mar 7, 2019
• Publicly Available Date: Mar 7, 2019
• 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: 57
• Issue: 7
• Pages: 4951-4966
• DOI: https://doi.org/10.1109/TGRS.2019.2894773
• Keywords: Super-resolution mapping; Image series; Spatial dependence; Temporal dependence
• Public URL: https://nottingham-repository.worktribe.com/output/1615740
• Publisher URL: https://ieeexplore.ieee.org/document/8677291
• Additional Information: © 2019 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.