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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...
This paper presents and evaluates the use of the maximum mutual information criterion to textural feature selection for satellite image classification. Our approach is based on a recent work of Mutual Information Feature Selector Algorithm. The effectiveness of the proposed approach is evaluated on real data. In fact, the textural features are extracted using the cooccurrence matrix from two forest...
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