Hyper-Spectral Imaging (HSI) is gaining, as a diagnostic tool in the field of cultural heritage, an increasing interest and it has been largely utilized in the last decade tanks to its ability to obtain both spatial and spectral information from a sample. Furthermore, it is a consolidated practice, to perform a better sample characterization, to acquire multiple imaging data coming from different devices covering different spectral ranges. In the present study, we present an analytical approach based on data fusion strategies to classify layers of different pigments using hyperspectral images acquired in two spectral ranges: Visible near infrared (Vis-NIR: 400-1000 nm) and short infrared wavelength infrared (SWIR: 1000-2500 nm). The main aim of the study was to combine the data acquired by the two HSI sensors following a multivariate approach to classify pigments, thanks to the complementary information collected in the different spectral regions.
Image data fusion applied to pictorial layers recognition / Bonifazi, G.; Capobianco, G.; Serranti, S.; Calvini, R.. - (2020), pp. 1-4. (Intervento presentato al convegno 2020 Italian Conference on Optics and Photonics, ICOP 2020 tenutosi a ita nel 2020) [10.1109/ICOP49690.2020.9300343].
Image data fusion applied to pictorial layers recognition
Calvini R.
2020
Abstract
Hyper-Spectral Imaging (HSI) is gaining, as a diagnostic tool in the field of cultural heritage, an increasing interest and it has been largely utilized in the last decade tanks to its ability to obtain both spatial and spectral information from a sample. Furthermore, it is a consolidated practice, to perform a better sample characterization, to acquire multiple imaging data coming from different devices covering different spectral ranges. In the present study, we present an analytical approach based on data fusion strategies to classify layers of different pigments using hyperspectral images acquired in two spectral ranges: Visible near infrared (Vis-NIR: 400-1000 nm) and short infrared wavelength infrared (SWIR: 1000-2500 nm). The main aim of the study was to combine the data acquired by the two HSI sensors following a multivariate approach to classify pigments, thanks to the complementary information collected in the different spectral regions.
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