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Probabilistic image classification using geological map units applied to land-cover change detection

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This paper describes how probabilistic methods provide a means to integrate analysis of remotely sensed imagery and geo-information processing. In a case study from southern Spain, geological map units were used to improve land-cover classification from Landsat TM imagery. Overall classification accurracy improved from 76% to 90% (1984) and from 64% to 69% (1995) when using stratification according to geology combined with iterative estimation of prior probabilities. Differences between the two years were mainly due to extremely dry conditions during the 1995 growing season. Per-pixel probabilities of class successions and entropy values calculated from the classification's posterior probability vectors served to quantify uncertainty in a post-classification comparison. It is concluded that iterative estimation of prior probabilities provides a practical approach to improve classification accuracy. Posterior probabilities of class membership provide useful information about the magnitude and spatial distribution of classification uncertainty.

Document Type: Research Article


Affiliations: 1: Wageningen Agricultural University, Department of Environmental Sciences, P.O. Box 339, 6700 AH Wageningen, The Netherlands 2: International Institute for Aerospace Survey and Earth Sciences (ITC), Department of Geoinformatics, PO Box 6, 7500 AA Enschede, The Netherlands

Publication date: 2000-08-15

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