A new hybrid spectral similarity measure for discrimination of Vigna species

The reflectance spectrum of the species in a hyperspectral data can be modelled as an n­dimensional vector. The spectral angle mapper computes the angle between the vectors which is used to discriminate the species. The spectral information divergence models the data as a probability distribution so that the spectral variability between the bands can be extracted using the stochastic measures. The hybrid approach of spectral angle mapper and spectral information divergence is found to be better discriminator than spectral angle mapper or spectral information divergence alone. The spectral correlation angle is computed as a cosine of the angle of the Pearsonian correlation coefficient between the vectors. The spectral correlation angle is a better measure than the spectral angle mapper as it considers only standardized values of the vectors rather than the absolute values of the vector. In the present paper a new hybrid measure is proposed which is based on the spectral correlation angle and the spectral information divergence. The proposed method has been compared with the hybrid approach of spectral information divergence and spectral angle mapper for discrimination of crops belonging to Vigna species using measures like 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 400nm­700nm spectral region. 1 Introduction The discrimination of targets is based on the comparison of the given spectra with the reference spectra available as end­members in a spectral library. The comparison is done using the similarity as a criterion (Chang 2000, Du et al 2004, Farifteh et al 2006, Van der Meer 2006). The spectral angle mapper represses the influence of shading to enhance the target reflectance because of which it has been extensively used for discrimination of targets like plant species (Bakker et al 2002, Clark 2005, Clark et al 1990, 1999). Stochastic measures such as spectral information divergence consider the spectral band­to­band variability as a result of uncertainty incurred by randomness. The spectrum can be modelled as a probability distribution so that the spectral properties can be further described by statistical moments of any order (Chang 2000). The hybrid approaches of spectral angle mapper and spectral information divergence