Autor(es): Chen, Ailin ; Jesus, Rui ; Vilarigues
Data: 2022
Identificador Persistente: http://hdl.handle.net/10400.21/14157
Origem: Repositório Científico do Instituto Politécnico de Lisboa
Assunto(s): Hyperspectral imaging; Deep learning; Convolutional neural networks; Visualisation; Pigment identification
This research presents the results of the implementation of deep learning neural networks in the identification of pure pigments of heritage artwork, namely paintings. Our paper applies an inno vative three-branch deep learning model to maximise the correct identification of pure pigments. The model proposed combines the feature maps obtained from hyperspectral images through multi ple convolutional neural networks, and numerical, hyperspectral metric data with respect to a set of reference reflectances. The results obtained exhibit an accurate representation of the pure pre dicted pigments which are confirmed through the use of analytical techniques. The model presented outperformed the compared coun terparts and is deemed to be an important direction, not only in terms of utilisation of hyperspectral data and concrete pigment data in heritage analysis, but also in the application of deep learning in other fields.
