Neural Networks for Hyperspectral Imaging of Historical Paintings: A Practical Review

Author:

Liu Lingxi1ORCID,Miteva Tsveta2ORCID,Delnevo Giovanni1ORCID,Mirri Silvia1ORCID,Walter Philippe3ORCID,de Viguerie Laurence3,Pouyet Emeline3

Affiliation:

1. Department of Computer Science and Engineering—Interdepartmental Centre for Industrial ICT Research (CIRI ICT), University of Bologna, 40126 Bologna, Italy

2. Laboratoire de Chimie Physique—Matière et Rayonnement (LCPMR), UMR 7614, CNRS, Sorbonne Université, 75005 Paris, France

3. Laboratoire d’Archéologie Moléculaire et Structurale (LAMS), CNRS, Sorbonne Université, 75005 Paris, France

Abstract

Hyperspectral imaging (HSI) has become widely used in cultural heritage (CH). This very efficient method for artwork analysis is connected with the generation of large amounts of spectral data. The effective processing of such heavy spectral datasets remains an active research area. Along with the firmly established statistical and multivariate analysis methods, neural networks (NNs) represent a promising alternative in the field of CH. Over the last five years, the application of NNs for pigment identification and classification based on HSI datasets has drastically expanded due to the flexibility of the types of data they can process, and their superior ability to extract structures contained in the raw spectral data. This review provides an exhaustive analysis of the literature related to NNs applied for HSI data in the CH field. We outline the existing data processing workflows and propose a comprehensive comparison of the applications and limitations of the various input dataset preparation methods and NN architectures. By leveraging NN strategies in CH, the paper contributes to a wider and more systematic application of this novel data analysis method.

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

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