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Geostatistical Solutions for Super-Resolution Land Cover Mapping.
- Source :
-
IEEE Transactions on Geoscience & Remote Sensing . Jan2008, Vol. 46 Issue 1, p272-283. 12p. 1 Black and White Photograph, 1 Diagram, 1 Chart, 1 Graph. - Publication Year :
- 2008
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01962892
- Volume :
- 46
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Geoscience & Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 28344046
- Full Text :
- https://doi.org/10.1109/TGRS.2007.907102