Hyperspectral Image Classification Using Multi-Class SLEX Model

In this paper, a new discrimination scheme is proposed for classifying multi-group hyperspectral image. The smooth localized complex exponentials (SLEX) library and a modified Bottom-Up Generalized Local Discriminant Bases (MGLDB-BU) algorithm are adopted for extracting ideal features for discrimination. With the extracted features, a mechanism based on Chernoff information is employed for classification. The effectiveness of the proposed scheme as compared to DAFE and NWFE is reported using real hyperspectral image dataset, Washington DC Mall.