Please use this identifier to cite or link to this item:
Title: Geostatistical solutions for super-resolution land cover mapping
Authors: Boucher, Alexandre 
Kyriakidis, Phaedon 
Cronkite-Ratcliff, Collin 
Keywords: Geostatistics
Spatial uncertainty
Subpixel mapping
Issue Date: Jan-2008
Publisher: IEEE
Source: IEEE Transactions on Geoscience and Remote Sensing, 2008, Volume 46, Issue 1, pages 272-283
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.
ISSN: 0196-2892
DOI: 10.1109/TGRS.2007.907102
Rights: © Copyright IEEE - All rights reserved.
Appears in Collections:Άρθρα/Articles

Show full item record

Citations 5

checked on Mar 6, 2017

Citations 5

checked on Mar 8, 2017

Page view(s) 50

checked on Apr 29, 2017

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.