A new hybrid spectral similarity measure for discrimination among Vigna species

Abstract

The reflectance spectrum of species in a hyperspectral data can be modelled as an n-dimensional vector. A spectral angle mapper (SAM) computes the angle between the vectors that is used to discriminate the species. Spectral information divergence (SID) models the data as a probability distribution so that the spectral variability between the bands can be extracted using stochastic measures. The hybrid approach of the SAM and SID is found to be a better discriminator than the SAM or SID on their own. The spectral correlation angle (SCA) is computed as a cosine of the angle of the Pearsonian correlation coefficient between the vectors. The SCA is a better measure than the SAM as it considers only standardized values of the vector rather than the absolute values of the vector. In the present article, we propose a new hybrid measure based on the SCA and the SID. The proposed method has been compared with the hybrid approach of the SID and SAM for discriminating species belonging to Vigna genus using measures such as relative spectral discriminatory power, relative discriminatory probability and relative discriminatory entropy in different spectral regions. Experimental results using the laboratory spectra show that the proposed method gives higher relative discriminatory power in the 400–700 nm spectral region.