Spectral discrimination method based on information divergence of gradient

A new method for spectral discrimination-spectral information divergence of spectral gradient [SID(SG)] is proposed. Firstly, the spectral gradients are estimated for discriminating the spectral local detailed characteristics, and then the information divergence of the spectral gradient is estimated for comparing their whole shape. The simulated spectra and the real measured are used as experimental data, the discrimination ability of the SID(SG) is compared to that of other methods, and relative spectral discriminatory entropy (RSDE) is used as standard to evaluate the experimental results quantitatively. RSDE values of the SID(SG) are 1.2849 and 1.5184, respectively, smaller than that of the several discrimination methods in each array. This indicates the superiority of SID(SG) over several other discrimination methods.