Geodaysit 2023

Francesco Immordino

Research Geologist


Sessions

06-13
09:45
15min
Hyperspectral PRISMA and Sentinel-2 Preliminary Assessment Comparison in Archaeological Sites
Sara Zollini, Francesco Immordino, Annachiara Dell'Acqua, Maria Alicandro, Elena Candigliota, Raimondo Quaresima

Over the last decades, remote sensing techniques have contributed to supporting cultural
heritage studies and management, including archaeological sites as well as their territorial context and
geographical surroundings. This paper aims to investigate the capabilities and limitations of the new
hyperspectral sensor PRISMA (Precursore IperSpettrale della Missione Applicativa) by the Italian
Space Agency (ASI), still little applied to archaeological studies. The PRISMA sensor was tested on
Italian terrestrial (Alba Fucens, Massa D’Albe, L’Aquila) and marine (Sinuessa, Mondragone, Caserta)
archaeological sites. A comparison between PRISMA hyperspectral imagery and the well-known
Sentinel-2 Multi-Spectral Instrument (MSI) was performed in order to better understand features and
outputs useful to investigate the aforementioned areas. At first, bad bands analysis and noise removal
were performed, in order to delete the numerically corrupted bands. Principal component analysis
(PCA) was carried out to highlight invisible details in the original image; then, spectral signatures of
representative areas were extracted and compared to Sentinel-2 data. At last, a classification analysis
(ML and SAM) was performed both on PRISMA and Sentinel-2 imagery. The results showed a full
agreement between Sentinel and PRISMA data, enhancing the capability of PRISMA in extrapolating
more spectral information and providing a better reliability in the extraction of the features.
these first analyses, applied in landscape archaeology studies, highlight
the great spectral operational capabilities of the PRISMA sensor. In future studies, a great
advantage can be brought by performing a reliable pansharpening in order to increase
the resolution of the final images (geometric resolution from pancromathic and spectral
resolution from hyperspectral data), as well as a more stable multitemporal acquisition in
the areas under investigation.

AIT Contribution
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