期刊
PATTERN RECOGNITION LETTERS
卷 25, 期 13, 页码 1491-1500出版社
ELSEVIER
DOI: 10.1016/j.patrec.2004.06.002
关键词
detection of land-cover transitions; change detection; multitemporal classification; multiple classifier systems; multilayer perceptron neural networks; radial basis function neural networks; k-nn technique; expectation-maximization algorithm; remote sensing images
This paper addresses the problem of detecting land-cover transitions by analysing multitemporal remote-sensing images. In order to develop an effective system for the detection of land-cover transitions, an ensemble of non-parametric multitemporal classifiers is defined and integrated in the context of a multiple classifier system (MCS). Each multitemporal classifier is developed in the framework of the compound classification (CC) decision rule. To develop as uncorrelated as possible classification procedures, the estimates of statistical parameters of classifiers are carried out according to different approaches (i.e., multilayer perceptron neural networks, radial basis functions neural networks, and k-nearest neighbour technique). The outputs provided by different classifiers are combined according to three standard stratcaies extended to the compound classification case (i.e., Majority voting, Bayesian average, and Bayesian,weighted average). Experiments, carried out on a multitemporal. remote-sensing data set, confirm the effectiveness of the proposed system. (C) 2004 Elsevier B.V. All rights reserved.
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