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Hyperspectral data consists of large number of images in narrow contiguous wavelength bands. In To reduce dimension of data, centroid amplitude coordinate of area under the spectral response curve (SRC) has been proposed, in this study, as feature. The methodology is based on dividing the area under SRC into subsets and calculating the centroid amplitude coordinate of each subset and thus, getting...
In this paper the potential of neural networks has been applied to hyperspectral data and exploited either for classification purposes or for data feature extraction and dimensionality reduction. For this latter task, a topology named autoassociative neural network has been used. In its complete form, the processing scheme uses a neural network architecture consisting of two stages: the first stage...
Recently, nonlinear feature extraction algorithms based on a so-called kernel trick have appeared to reduce the limitations of linear feature extraction methods with respect to class discrimination. This study presents a new kernel function that integrates the discriminative information from class labels and spatial contexts into the basic radial basis function (RBF). We represent the mutual closeness...
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