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Proceedings Paper

RBF neural network approach for detecting land-cover transitions
Author(s): Lorenzo Bruzzone; Roberto Cossu
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Paper Abstract

The problem of detecting land-cover transitions in multitemporal remote-sensing images by exploiting the Bayes rule for compound classification is addressed. In particular, a novel method is proposed, which is based on the use of radial basis function (RBF) neural networks. The proposed method is composed of three main steps: i) the statistical terms involved in the compound classification rule are estimated separately on each single image by using the available training set and a standard procedure for the training of RBF neural networks; ii) the joint class probabilities are roughly initialised according to a simple estimation procedure; iii) the estimation of all parameters of the networks are iteratively improved by formulating the estimation process in terms of expectation-maximisation algorithm. The main advantages of the proposed approach with respect to our previous work on this topic are the following: i) to jointly perform and optimise the estimation process of all the required statistical terms; ii) to use both labelled and unlabeled samples in the estimation of all the required statistical terms (this is made possible by the use of a specific RBF architecture); iii) to better model the temporal correlation between classes in multitemporal images by replacing the class conditional independence assumption with a less restrictive one. Experiments, carried out on a multitemporal remote-sensing data set, confirm the effectiveness of the proposed automatic approach.

Paper Details

Date Published: 28 January 2002
PDF: 9 pages
Proc. SPIE 4541, Image and Signal Processing for Remote Sensing VII, (28 January 2002); doi: 10.1117/12.454156
Show Author Affiliations
Lorenzo Bruzzone, Univ. degli Studi di Trento (Italy)
Roberto Cossu, Univ. degli Studi di Trento (Italy)

Published in SPIE Proceedings Vol. 4541:
Image and Signal Processing for Remote Sensing VII
Sebastiano Bruno Serpico, Editor(s)

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