  The reflectance spectrum of the species in a hyperspectral data can be
modelled as an n-dimensional 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 400nm-700nm spectral region.
