Proceedings of the KSRS Conference (대한원격탐사학회:학술대회논문집)
- 2003.11a
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- Pages.402-404
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- 2003
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- 1226-9743(pISSN)
Unsupervised Change Detection Using Iterative Mixture Density Estimation and Thresholding
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Park, No-Wook
(Geoscience Information Center, Korea Institute of Geoscience and Mineral Resources) ;
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Chi, Kwang-Hoon
(Geoscience Information Center, Korea Institute of Geoscience and Mineral Resources)
- Published : 2003.11.03
Abstract
We present two methods for the automatic selection of the threshold values in unsupervised change detection. Both methods consist of the same two procedures: 1) to determine the parameters of Gaussian mixtures from a difference image or ratio image, 2) to determine threshold values using the Bayesian rule for minimum error. In the first method, the Expectation-Maximization algorithm is applied for estimating the parameters of the Gaussian mixtures. The second method is based on the iterative thresholding that successively employs thresholding and estimation of the model parameters. The effectiveness and applicability of the methods proposed here are illustrated by an experiment on the multi-temporal KOMPAT-1 EOC images.