Hyperspectral Band Selection Based on Endmember Dissimilarity for Hyperspectral Unmixing

Hyperspectral remote sensing could acquire hundreds of bands to cover a complete spectral interval, which deliver more information and allow a whole range of new and more precise applications. But vast data volume can cause trouble in computer processing and data transmission. Too many bands may cause interference for image processing and endmember variability is inevitable in hyperspectral data, which will affect the accuracy of interpretation. Band selection for hyperspectral image data is an effective way to mitigate the curse of dimensionality. In this paper, one hyperspectral band selection method based on endmember dissimilarity is proposed. This method used Mahalanobis distance as class separability criterion, and the spectral signature for each class is proposed by endmember extraction method automatically. Experiments on both synthetic and real hyperspectral data sets indicate that the proposed method outperformed the Minimum Estimated Abundance Covariance (MEAC) and Uniform Spectral Spacing (USS) method.