Pseudo-Divergence and Bidimensional Histogram of Spectral Differences for Hyperspectral Image Processing
Résumé
Spectral Information Divergence (SID) was identified as the most efficient spectral similarity measure. However, we show that divergence are not adapted to direct use on spectra. Following an idea proposed by Nidamanuri, we construct a spectral pseudo-divergence based on the Kullback-Leibler divergence. This pseudo-divergence is composed of two parts: a shape and an intensity similarity measure. Consequently, bidimensional representation of spectral differences are constructed to display the histograms of similarity between a spectral reference and the spectra from a data-set or an hyperspectral image. We prove the efficiency of the spectral similarity measure and of the bidimensional histogram of spectral differences on artificial and Cultural Heritage spectral images.
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