Unmixing microinfrared spectroscopic images of cross-sections of historical oil paintings

TL;DR

This paper introduces an unsupervised CNN autoencoder approach for blind unmixing of hyperspectral infrared images of historical paintings, enabling more accurate, scalable, and automated material analysis of complex, multi-layered art samples.

Contribution

The paper presents a novel unsupervised deep learning method with a weighted spectral angle loss for unmixing hyperspectral infrared images, improving interpretability and robustness in heritage science applications.

Findings

Effective unmixing of complex hyperspectral images demonstrated on a Van Eyck painting cross-section.

Weighted spectral angle loss enhances interpretability in contaminated spectral regions.

Method reduces reliance on manual spectral library comparison.

Abstract

Spectroscopic imaging (SI) has become central to heritage science because it enables non-invasive, spatially resolved characterisation of materials in artefacts. In particular, attenuated total reflection Fourier transform infrared microscopy (ATR-$\mu$FTIR) is widely used to analyse painting cross-sections, where a spectrum is recorded at each pixel to form a hyperspectral image (HSI). Interpreting these data is difficult: spectra are often mixtures of several species in heterogeneous, multi-layered and degraded samples, and current practice still relies heavily on manual comparison with reference libraries. This workflow is slow, subjective and hard to scale. We propose an unsupervised CNN autoencoder for blind unmixing of ATR-$\mu$FTIR HSIs, estimating endmember spectra and their abundance maps while exploiting local spatial structure through patch-based modelling. To reduce sensitivity to atmospheric and acquisition artefacts across $>1500$ bands, we introduce a weighted spectral angle distance (WSAD) loss with automatic band-reliability weights derived from robust measures of spatial flatness, neighbour agreement and spectral roughness. Compared with standard SAD training, WSAD improves interpretability in contamination-prone spectral regions. We demonstrate the method on an ATR-$\mu$FTIR cross-section from the Ghent Altarpiece attributed to the Van Eyck brothers.