A Band Selection Method For Hyperspectral Images Using Choquet Fuzzy Integral

Hyperspectral remote sensing images provide richer information about materials than that of multispectral images. The new larger data volumes of hyperspectral sensors bring new challenges for traditional image processing techniques. Therefore, conventional classification methods could fail without employing dimension reduction preprocessing. The dimensional reduction methods can be totally divided into two classes: feature extraction and feature selection. In this paper, a new feature selection method for hyperspectral images is proposed, which colligates the information entropy, classification separability and correlation coefficients with the Choquet fuzzy integral to select the bands. Experiments on the AVIRIS dataset show that the proposed method removes the redundant spectral bands effectively.