Adaptive band selection for hyperspectral image fusion using mutual information

Hyperspectral imagery consists of hundreds of spectra or bands whose intensity is measured at various wavelength. Fusing the multiple spectral bands can provide more potential to differentiate between natural and man-made objects, and significantly improve the capability of target detection and classification. Spectral band or wavelength selection is one of the fundamental problems in hyperspectral data fusion. It is one instance of the classical optimal subset selection problem, which is known to be computationally hard. In this paper, we propose a new information-based band selection method for hyperspectral image fusion, which uses an adaptive measurement of mutual information (MI). As derived from the concept of entropy, MI measures the statistical dependence between two random variables and therefore can be used to evaluate the relative utility of each band to classification. Experiments on the AVIRIS dataset show that the method effectively identifies redundant spectral bands. Removing 15% of the total bands increases accuracy by 1.76% relative to performance on all bands, whereas removing 45% of the bands gives only 1.34% loss of accuracy.