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Support Vector classifiers for Land Cover Classification
 Mahesh Pal Lecturer, Department of Civil Engineering National Institute of Technology Kurukshetra 136119 Haryana (India) Email: mpce_pal@yahoo.co.uk Fax No. 01744 38050  Paul M Mather Prof., School of geography University of Nottingham University Park Nottingham, NG7 2RD, UK Email: paul.mather@nottingham.ac.uk
1. Introduction
Much research effort in the past ten years has been devoted to analysis of the performance of artificial neural networks in image classification (Benediktsson et al., 1990; Heermann and Khazenie, 1992). The preferred algorithm is feed-forward multi-layer perceptron using back-propagation, due to its ability to handle any kind of numerical data, and to its freedom from distributional assumptions. Although neural networks may generally be used to classify data at least as accurately as statistical classification approaches a number of studies have reported that users of neural classifiers have problems in setting the choice of various parameters during training (Wilkinson, 1997). The choice of architecture of the network, the sample size for training, learning algorithms, and number of iterations required for training are some of these problems. A new classification system based on statistical learning theory (Vapnik, 1995), called the support vector machine has recently been applied to the problem of remote sensing data classification (Huang et al., 2002; Zhu and Blumberg, 2002; Gualtieri and Cromp, 1998). This technique is said to be independent of the dimensionality of feature space as the main idea behind this classification technique is to separate the classes with a surface that maximise the margin between them, using boundary pixels to create the decision surface. The data points that are closest to the hyperplane are termed "support vectors". The number of support vectors is thus small as they are points close to the class boundaries (Vapnik, 1995). One major advantage of support vector classifiers is the use of quadratic programming, which provides global minima only. The absence of local minima is a significant difference from the neural network classifiers. 2. Classification Methods Three classification algorithms used for this study are the maximum-likelihood, multi-layer backpropagation neural network and support vector classifier. A brief summary of these classifiers is given below. 2.1 Maximum-Likelihood Classifier The Maximum likelihood Classifier (MLC) is based on the assumption that the members of each class are normally distributed in feature space. MLC is a pixel-based method and can be defined as follows: a pixel with an associated observed feature vector X is assigned to class c if X Î if > for all j¹ k, j, k = 1,……, N For multivariate Gaussian distributions is given by: 
where and are the sample mean vector and covariance matrix of class k, and is the discriminating function. Implementation of the MLC involves the estimation of class mean vectors and covariance matrices using training patterns chosen from known examples of each particular class.
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