A Virtual Dimensionality Method for Hyperspectral Imagery

Hyperspectral sensors capture images in hundreds of narrow spectral channels. The spectral signatures for each spatial location provide rich information about an image scene, leading to better "separation" between physical materials and objects. Hyperspectral data are spectrally overestimated and the useful signals usually occupy lower dimensional subspace which needs to be inferred. The signal information is usually concentrated in lower dimensional subspaces. For estimating the number of spectrally distinct signatures present in a hyperspectral data the concept of Virtual Dimensionality (VD), give us the minimum number of spectrally distinct signal sources that characterize this data from a perspective view of target detection and classification. Considering these facts it is important to reduce the volume of data with minimum loss of information and this is the main idea of our algorithm. In this paper, a new VD modified method for estimating the number of spectral distinct signatures has been proposed. To demonstrate the applicability of the developed software tools, we used a few well-known hyperspectral image-data sets. (C) 2015 The Authors. Published by Elsevier Ltd.