Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/8677
Title: Geostatistical solutions for super-resolution land cover mapping
Authors: Boucher, Alexandre 
Kyriakidis, Phaedon 
Cronkite-Ratcliff, Collin 
Major Field of Science: Engineering and Technology
Field Category: Environmental Engineering
Keywords: Geostatistics;Spatial uncertainty;Subpixel mapping
Issue Date: Jan-2008
Source: IEEE Transactions on Geoscience and Remote Sensing, 2008, vol. 46, iss. 1, pp. 272-283
Volume: 46
Issue: 1
Start page: 272
End page: 283
Journal: IEEE Transactions on Geoscience and Remote Sensing 
Abstract: Super-resolution land cover mapping aims at producing fine spatial resolution maps of land cover classes from a set of coarse-resolution class fractions derived from satellite information via, for example, spectral unmixing procedures. Based on a prior model of spatial structure or texture that encodes the expected patterns of classes at the fine (target) resolution, this paper presents a sequential simulation framework for generating alternative super-resolution maps of class labels that are consistent with the coarse class fractions. Two modes of encapsulating the prior structural information are investigated—one uses a set of indicator variogram models, and the other uses training images. A case study illustrates that both approaches lead to super-resolution class maps that exhibit a variety of spatial patterns ranging from simple to complex. Using four different examples, it is demonstrated that the structural model controls the patterns seen on the super-resolution maps, even for cases where the coarse fraction data are highly constraining.
URI: https://hdl.handle.net/20.500.14279/8677
ISSN: 01962892
DOI: 10.1109/TGRS.2007.907102
Rights: © IEEE
Type: Article
Affiliation : Stanford University 
University of California Santa Barbara 
Publication Type: Peer Reviewed
Appears in Collections:Άρθρα/Articles

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