An Experiment-based Comparative Analysis of Pigment Classification Algorithms using Hyperspectral Imaging

Abstract

Hyperspectral imaging techniques are widely used in cultural heritage for documentation and material analysis. Pigment classification of an artwork is an essential task. Several algorithms have been used for hyperspectral data classification, and the effectiveness of each algorithm depends on the application domain. However, very few have been applied for pigment classification tasks in the cultural heritage domain. Most of these algorithms work effectively for spectral shape differences and might not perform well for spectra with differences in magnitude or for spectra that are nearly similar in shape but might belong to two different pigments. In this work, we evaluate the performance of different supervised-based algorithms and few machine learning models for the pigment classification of a mockup using hyperspectral imaging. The result obtained shows the importance of choosing appropriate algorithms for pigment classification.