Detection of land-cover transitions by combining multidate classifiers

作     者：Bruzzone, L Cossu, R Vernazza, G 

作者机构：Univ Trent Dept Informat & Commun Technol I-38050 Trento Italy Univ Genoa Dept Biophys & Elect Engn I-16145 Genoa Italy 

出 版 物：《PATTERN RECOGNITION LETTERS》 (模式识别快报)

年 卷 期：2004年第25卷第13期

页      面：1491-1500页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：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 

摘      要：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.