Hyperspectral PRISMA and Sentinel-2 Preliminary Assessment Comparison in Alba Fucens and Sinuessa Archaeological Sites (Italy)

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 hype...

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Veröffentlicht in:Land (Basel) 2022-11, Vol.11 (11), p.2070
Hauptverfasser: Alicandro, Maria, Candigliota, Elena, Dominici, Donatella, Immordino, Francesco, Masin, Fabrizio, Pascucci, Nicole, Quaresima, Raimondo, Zollini, Sara
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Sprache:eng
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Zusammenfassung: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.
ISSN:2073-445X
2073-445X
DOI:10.3390/land11112070