The rapid advances in hyperspectral sensing technology have made it possible to collect remote-sensing data in hundreds of bands. However, the data-analysis methods that have been successfully applied to multispectral data are often limited in achieving satisfactory results for hyperspectral
data. The major problem is the high dimensionality, which deteriorates the classification due to the Hughes Phenomenon. In order to avoid this problem, a large number of algorithms have been proposed, so far, for feature reduction. Based on the concept of multiple classifiers, we propose a
new schema for the feature selection procedure. In this framework, instead of using feature selection for whole classes, we adopt feature selection for each class separately. Thus different subsets of features are selected at the first step. Once the feature subsets are selected, a Bayesian
classifier is trained on each of these feature subsets. Finally, a combination mechanism is used to combine the outputs of these classifiers. Experiments are carried out on an Airborne Visible/Infrared Imaging Spectroradiometer (AVIRIS) data set. Encouraging results have been obtained in terms
of classification accuracy, suggesting the effectiveness of the proposed algorithms.
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Document Type: Research Article
Department of Geomatics Engineering,University of Calgary, CalgaryABT2N 1N4, Canada
Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Vali-e-Asr St., Mirdamad CrossTehran, Iran
August 10, 2011
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