An Algorithm and Heuristic based on Normalized Mutual Information for Dimensionality Reduction and Classification of Hyperspectral images
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In the feature classification domain, the choice of data affects widely the results. The Hyperspectral image (HSI), is a set of more than a hundred bidirectional measures (called bands), of the same region (called ground truth map: GT). The HSI is modelized at a set of N vectors. So we have N features (or attributes) expressing N vectors of measures for C substances (called classes). The problematic is that it's pratically impossible to investgate all possible subsets. So we must find K vectors among N, such as relevant and no redundant ones; in order to classify substances. Here we introduce an algorithm based on Normalized Mutual Information to select relevant and no redundant bands, necessary to increase classification accuracy of HSI. Keywords: Feature Selection, Normalized Mutual information, Hyperspectral images, Classification, Redundancy.
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