## 2 Materials and Methods
### LIF Sensor  
The second technology applied is based on the phenomenon of fluorescence induced by an ultraviolet laser and it is used to characterise the surface from a chemical point of view. Due to the large dimensions of the areas to be investigated, for the present work, among the available LIF prototypes available at the Diagnostic and Metrology Laboratory of the ENEA research centre of Frascati (Rome, Italy), a portable scanning system for rapid stand-off analyses has been used; it is based on technical solutions that can assure the ability to scan about 100 square meters in about 30 min with a spatial resolution of about 1 cm (Figure 3 right).  
The main components of the prototype are a laser source at 248 nm with high pulse repetition rate up to 500 Hz, spot size of 6 mm \(\times\) 3 mm and pulse energy of 16 mJ, an optical system, including a beam expander 8\(\times\) and a motorised mirror, driving the laser beam to the target, lenses to collect the signal from the target and finally an ICCD (Intensified CCD) detector, with a fixed position and a large field of view, providing 2D fluorescence images. A filter wheel is mounted in front of the ICCD to detect fluorescence images at 8 preselected spectral bands identified by a previous study on materials of interest [30]. A LabView custom-made software allows the user to set experimental parameters, to define the scene to be scanned, to control the different components of the system, to acquire data and to perform preliminary fast data processing. Fluorescence images can be reconstructed in false colours by using the three most intense or interesting detected spectral features, associated respectively with red, green, and blue channels (RGB) or they can be processed to highlight only specific surface features. More details on the LIF sensor used are presented in [31]. Since the targets run no risk during the measurements, which take place from large distances (up to 10 m), without physical contact and without the need for scaffolding, the technique is particularly suitable for studying works of art, obtaining fluorescence maps and, therefore, compositional maps, for identifying the different materials present on the surface and highlighting features not visible by the naked eye, such as restorations, integrations, damages, and biological attacks.  
The data fusion between RGB-ITR and LIF maps was performed by operating a manual detection of common points visible in both datasets. Applying affine matrix transformations, it was possible to spread the LIF maps over the RGB-ITR textures. This step is fundamental not only for overimposing information coming out from different diagnostic devices but also for processing algorithms that can correlate this information together for segmenting the scenes and classifying materials, biological attacks and structural defects.  
The data fusion between RGB-ITR and LIF maps was obtained by using an ad hoc software called ITR-Analyzer (version 0.9). This software is a GUI based on MATLAB (version R2022B), and it is composed of several scripts, able to import and manipulate the data collected by the laser scanner RGB-ITR. This software is also able to import external data and superimpose them on the 3D laser-coloured point cloud collected by the ITR scanner. The script opens an interaction dialogue where the user can load the ITR coloured matrix and the external data, in this case, the LIF maps. After the user selects the common points between the two matrices, the algorithms process the best affine matrix that allows the LIF maps to spread over the ITR coloured matrix. If the result is correct, the multispectral matrices can be saved in an image file format and can be used as texture to load on the 3D model obtained by triangulating the point cloud collected by the ITR scanner. This result leads to the inspection of the digitalised scene with localised visible and invisible information, useful for segmenting and classifying materials, biological attacks and structural defects.