Research progress on spectral similarity metrics

Spectral similarity metrics are important in the field of hyperspectral data analysis. To further analyze their application in hyperspectral image processing, the current binary spectral similarity metrics method based on distance or projection was summarized. The problems in the binary spectral similarity metrics were analyzed and discussed. Then, this study chiefly introduced a multiple spectral similarity metric called N-dimensional solid spectral angle (NSSA). The NSSA method breaks through the limitation of the traditional binary spectral angle mapping in essence, which can not only calculate the angle between two spectra but also the angle constructed by multiple spectra jointly in Euclidean space. The method provides a quantitative measure to evaluate the joint similarity of multivariate spectra. The potential research value and applications of the NSSA method in hyperspectral band selection and endmembers extraction were analyzed and forecasted. The analysis indicates that the NSSA method can better realize the spectral similarity measure and has high research value in the field of hyperspectral imaging process.