Abstract: Hyperspectral remote sensing data contain abundant spectral information,which are widely used,but sometimes the redundant spectral information limits the classification accuracy and computational complexity.In order to improve the interpretation efficiency,hyperspectral image dimension reduction is necessary,which is also one of the highlights in the hyperspectral image processing.This paper presented a hyperspectral image band selection method based on endmember separability (EBSS).This method maximizes class separability using Mahalanobis distance to determine the optimal band combination.Compared with other supervision of band selection algorithms,the proposed method does not need the training samples,and does not conduct classification during band selection.The experiment results show that the proposed method is effective and can get better classification accuracy.

Key words: Supervised band selection,Hyperspectral image,Class separability