Abstract
Selecting the most informative features from high dimensional space is one of the well-known problems in multispectral image classification and pattern recognition applications. The commonly used techniques for dimensionality reduction are the Principal Components Analysis (PCA) and the Linear Discriminant Analysis (LDA). However, their components are not necessarily the best for such classification. In this work, we investigate the effectiveness of two Mutual Information Feature Selector (MIFS) algorithms. The objective is to identify the most optimal algorithm that reduce the important dimension of input textural feature space while keeping the highest accuracy classification. The candidate textural features are extract, from an HRV-XS SPOT image of forest region in Rabat, Morocco, using Wavelet Transform (WT) at level (l=1,2). Experimental results prove that MIFS algorithms give a better performances, in terms of dimensionality reduction and classification accuracy, than classical methods PCA and LDA. The retained classifier is the Support Vectors Machine (SVM).
Published Version
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