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 |
CORE Recommender
Items in KTISIS are protected by copyright, with all rights reserved, unless otherwise indicated.