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http://dx.doi.org/10.7780/kjrs.2004.20.6.383

Assessing Spatial Uncertainty Distributions in Classification of Remote Sensing Imagery using Spatial Statistics  

Park No-Wook (Geoscience Information Center, Korea Institute of Geoscience and Mineral Resources)
Chi Kwang-Hoon (Geoscience Information Center, Korea Institute of Geoscience and Mineral Resources)
Kwon Byung-Doo (Department of Earth Sciences, Seoul National University)
Publication Information
Korean Journal of Remote Sensing / v.20, no.6, 2004 , pp. 383-396 More about this Journal
Abstract
The application of spatial statistics to obtain the spatial uncertainty distributions in classification of remote sensing images is investigated in this paper. Two quantitative methods are presented for describing two kinds of uncertainty; one related to class assignment and the other related to the connection of reference samples. Three quantitative indices are addressed for the first category of uncertainty. Geostatistical simulation is applied both to integrate the exhaustive classification results with the sparse reference samples and to obtain the spatial uncertainty or accuracy distributions connected to those reference samples. To illustrate the proposed methods and to discuss the operational issues, the experiment was done on a multi-sensor remote sensing data set for supervised land-cover classification. As an experimental result, the two quantitative methods presented in this paper could provide additional information for interpreting and evaluating the classification results and more experiments should be carried out for verifying the presented methods.
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