Detection of land-cover transitions by combining multidate classifiers

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.