Multispectral imaging for the documentation of graffiti in an urban environment
[EN] Multispectral imaging (MSI) is increasingly used for the documentation and analysis of cultural heritage. It provides conservators a powerful non-destructive technique (NDT) and non-contact tool for detecting damage, hidden features and material-specific characteristics. Hereby multispectral do...
| Autores: | , |
|---|---|
| Tipo de recurso: | capítulo de libro |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/192192 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/192192 |
| Access Level: | acceso abierto |
| Palabra clave: | Multispectral imaging (MSI) Non destructive techniques Cultural heritage Preventive conservation Multispectral/multiband imaging Wall paintings |
| Sumario: | [EN] Multispectral imaging (MSI) is increasingly used for the documentation and analysis of cultural heritage. It provides conservators a powerful non-destructive technique (NDT) and non-contact tool for detecting damage, hidden features and material-specific characteristics. Hereby multispectral documentation of wall paintings in an urban environment poses special challenges for the art expert. For example, these are often large works of art located outdoors on building façades. Excitation with artificial light in well-defined spectral ranges, as should ideally be the case in MSI, is therefore often not possible. In the following, low-cost variants of MSI (ultraviolet reflectography, visible light imaging and infrared reflectography) in combination with 3D photogrammetry and statistical methods for analysing image data are tested and discussed. Hereby, a metrically correct, large-scale documentation of wall paintings with accurate superimposed images of different spectral ranges will be generated by linking the MSI data in a photogrammetric image cluster to create individual texture maps for each spectral band. Furthermore, Principal Component Analysis (PCA) is used to extract additional information from the MSI data. The case studies are located on the campus of the Universitat Politècnica de València. |
|---|