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Hyperspectral Sensors for the Characterization of Cultural Heritage Surfaces

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Sensing the Past

Part of the book series: Geotechnologies and the Environment ((GEOTECH,volume 16))

Abstract

The characterization of artistic and historical surfaces in a wide, fast, low-expense, and noninvasive way is a necessity for the conservation of these cultural assets. Hyperspectral sensors having bands in the visible-near infrared and short-wave infrared (VNIR-SWIR) regions are commonly used for determining the characteristics and properties of many materials (such as soils, minerals, rocks, water, vegetation) because of their ability to provide information in a fast and nondestructive way. Among the existing VNIR-SWIR techniques, field spectroscopy and imaging spectroscopy (remote sensing) have a crucial part in the characterization of different kinds of surfaces. In this work, the potentialities of hyperspectral sensors (working in the range 0.35–2.5 μm) for cultural heritage applications are discussed. The attention is focused both on field spectroscopy as a method for accurate characterization of small, confined, and highly heterogeneous surfaces and on imaging spectrometry obtained through field sensors. A few case studies where both techniques were employed are also reported.

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Acknowledgments

The authors wish to thank the teams that have worked on the collection of many field and laboratory data, namely, Silvia Vettori, Elena Pecchioni, Teresa Salvatici, Leandro Chiarantini, Francesca Serraglini, Diletta Zecchi, and Cong Wang. A part of this work was supported by Regione Toscana in the framework of the agreement for Research and Technological transfer to productive system between the Italian Government and Regione Toscana (SKY-EYE project).

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Correspondence to Mara Camaiti .

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Camaiti, M., Benvenuti, M., Costagliola, P., Di Benedetto, F., Moretti, S. (2017). Hyperspectral Sensors for the Characterization of Cultural Heritage Surfaces. In: Masini, N., Soldovieri, F. (eds) Sensing the Past. Geotechnologies and the Environment, vol 16. Springer, Cham. https://doi.org/10.1007/978-3-319-50518-3_13

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