Detection of land-cover transitions by combining multidate classifiers

Authors:
L. Bruzzone;R. Cossu;G. Vernazza
Affiliations:
Department of Information and Communication Technology, University of Trento, Via Sommarive 14, I-38050 Povo, Trento, Italy;Department of Information and Communication Technology, University of Trento, Via Sommarive 14, I-38050 Povo, Trento, Italy;Department of Biophysical and Electronic Engineering, University of Genoa, Via Opera Pia 11 A, I-16145 Genova, Italy
Venue:
Pattern Recognition Letters - Special issue: Pattern recognition for remote sensing (PRRS 2002)
Year:
2004

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Abstract

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 strategies 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.