Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/20694
Title: Indicator cokriging-based subpixel mapping without prior spatial structure information
Authors: Wang, Q
Atkinson, PM
Shi, W 
Keywords: Indicator cokriging (ICK)
Land cover mapping
Semivariogram
Subpixel mapping (SPM)
Super-resolution mapping
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on geoscience and remote sensing, 2015, v. 53, no. 1, 2321834, p. 309-323 How to cite?
Journal: IEEE transactions on geoscience and remote sensing 
Abstract: Indicator cokriging (ICK) has been shown to be an effective subpixel mapping (SPM) algorithm. It is noniterative and involves few parameters. The original ICK-based SPM method, however, requires the semivariogram of land cover classes from prior information, usually in the form of fine spatial resolution training images. In reality, training images are not always available, or laborious work is needed to acquire them. This paper aims to seek spatial structure information for ICK when such prior land cover information is not obtainable. Specifically, the fine spatial resolution semivariogram of each class is estimated by the deconvolution process, taking the coarse spatial resolution semivariogram extracted fromthe class proportion image as input. The obtained fine spatial resolution semivariogram is then used to estimate class occurrence probability at each subpixel with the ICK method. Experiments demonstrated the feasibility of the proposed ICK with the deconvolution approach. It obtains comparable SPM accuracy to ICK that requires semivariogram estimated from fine spatial resolution training images. The proposed method extends ICK to cases where the prior spatial structure information is unavailable.
URI: http://hdl.handle.net/10397/20694
ISSN: 0196-2892
EISSN: 1558-0644
DOI: 10.1109/TGRS.2014.2321834
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