Please use this identifier to cite or link to this item: http://hdl.handle.net/2067/51354
Title: Using PRISMA Hyperspectral Data for Land Cover Classification with Artificial Intelligence Support
Authors: Delogu, Gabriele
Caputi, Eros
Perretta, Miriam
Ripa, Maria Nicolina 
Boccia, Lorenzo
Journal: SUSTAINABILITY 
Issue Date: 2023
Abstract: 
Hyperspectral satellite missions, such as PRISMA of the Italian Space Agency (ASI), have
opened up new research opportunities. Using PRISMA data in land cover classification has yet to be
fully explored, and it is the main focus of this paper. Historically, the main purposes of remote sensing
have been to identify land cover types, to detect changes, and to determine the vegetation status of
forest canopies or agricultural crops. The ability to achieve these goals can be improved by increasing
spectral resolution. At the same time, improved AI algorithms open up new classification possibilities.
This paper compares three supervised classification techniques for agricultural crop recognition using
PRISMA data: random forest (RF), artificial neural network (ANN), and convolutional neural network
(CNN). The study was carried out over an area of 900 km2 in the province of Caserta, Italy. The
PRISMA HDF5 file, pre-processed by the ASI at the reflectance level (L2d), was converted to GeoTiff
using a custom Python script to facilitate its management in Qgis. The Qgis plugin AVHYAS was used
for classification tests. The results show that CNN gives better results in terms of overall accuracy
(0.973), K coefficient (0.968), and F1 score (0.842).
URI: http://hdl.handle.net/2067/51354
ISSN: 1937-0709
DOI: 10.3390/su151813786
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections:A1. Articolo in rivista

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