An elliptical basis function network for classification of remote sensing images

Jian-Cheng Luo (), Yee Leung, Jiang Zheng and Jiang-Hong Ma

Journal of Geographical Systems, 2004, vol. 6, issue 3, 219-236

Abstract: An elliptical basis function (EBF) network is employed in this study for the classification of remotely sensed images. Though similar in structure, the EBF network differs from the well-known radial basis function (RBF) network by incorporating full covariance matrices and employing the expectation-maximization (EM) algorithm to estimate the basis functions. Since remotely sensed data often take on mixture-density distributions in the feature space, the network not only possesses the advantage of the RBF mechanism, but also utilizes the EM algorithm to compute the maximum likelihood estimates of the mean vectors and covariance matrices of a Gaussian mixture distribution in the training phase. Experimental results show that the EM-based EBF network is more effective in training and simpler in structure than an RBF network constructed for the same task. Copyright Springer-Verlag Berlin Heidelberg 2004

Keywords: Neural networks; classification; elliptical basis functions; EM algorithm; mixture densities; radial basis functions; remotely sensed image; R14; R52; Q24 (search for similar items in EconPapers)
Date: 2004
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DOI: 10.1007/s10109-004-0136-1

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