Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/14387
Title: Super-resolution land cover mapping with indicator geostatistics
Authors: Boucher, Alexandre
Kyriakidis, Phaedon 
Major Field of Science: Engineering and Technology
Field Category: Civil Engineering
Keywords: Downscaling;Indicator Kriging;Indicator variograms;Inverse problems;Spatial uncertainty;Sub-pixel mapping
Issue Date: 15-Oct-2006
Source: Remote Sensing of Environment, Volume 104, Issue 3, 15 October 2006, Pages 264-282
Volume: 104
Issue: 3
Journal: Remote Sensing of Environment 
Abstract: Many satellite images have a coarser spatial resolution than the extent of land cover patterns on the ground, leading to mixed pixels whose composite spectral response consists of responses from multiple land cover classes. Spectral unmixing procedures only determine the fractions of such classes within a coarse pixel without locating them in space. Super-resolution or sub-pixel mapping aims at providing a fine resolution map of class labels, one that displays realistic spatial structure (without artifact discontinuities) and reproduces the coarse resolution fractions. In this paper, existing approaches for super-resolution mapping are placed within an inverse problem framework, and a geostatistical method is proposed for generating alternative synthetic land cover maps at the fine (target) spatial resolution; these super-resolution realizations are consistent with all the information available. More precisely, indicator coKriging is used to approximate the probability that a pixel at the fine spatial resolution belongs to a particular class, given the coarse resolution fractions and (if available) a sparse set of class labels at some informed fine pixels. Such Kriging-derived probabilities are used in sequential indicator simulation to generate synthetic maps of class labels at the fine resolution pixels. This non-iterative and fast simulation procedure yields alternative super-resolution land cover maps that reproduce: (i) the observed coarse fractions, (ii) the fine resolution class labels that might be available, and (iii) the prior structural information encapsulated in a set of indicator variogram models at the fine resolution. A case study is provided to illustrate the proposed methodology using Landsat TM data from SE China. © 2006 Elsevier Inc. All rights reserved.
URI: https://ktisis.cut.ac.cy/handle/10488/14387
ISSN: 00344257
DOI: 10.1016/j.rse.2006.04.020
Type: Article
Affiliation : Stanford University 
University of California Santa Barbara 
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