Semantic Segmentation of Crops via Hyperspectral PRISMA Satellite Images

Manilo Monaco, Angela Sileo, Diana Orlandi, Maria Battagliere, Laura Candela, Mario Cimino, Gaetano Vivaldi, Vincenzo Giannico

2024

Abstract

Data from hyperspectral remote sensing are promising to extract and classify crop characteristics, because it provides accurate and continuous spectral signatures of crops. This paper focuses on data acquired by PRISMA, a high-resolution hyperspectral imaging satellite. Due to this large data availability, huge training datasets can be built to feed modern deep learning algorithms. This paper shows a spectral-temporal data processing based on random forest to perform feature selection, and on two-dimensional convolutional neural network to carry out classification of crops, exploiting variations in respective phenological phases during the annual life cycle. The proposed solution is described via a pilot case study, involving a field farmed with olive groves and vineyards in Apulia, Italy. Moreover, one-dimensional convolutional neural networks are used to compare classification accuracies. Early results are promising with respect to the literature.

Paper Citation

in Harvard Style

Monaco M., Sileo A., Orlandi D., Battagliere M., Candela L., Cimino M., Vivaldi G. and Giannico V. (2024). Semantic Segmentation of Crops via Hyperspectral PRISMA Satellite Images. In Proceedings of the 10th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM; ISBN 978-989-758-694-1, SciTePress, pages 187-194. DOI: 10.5220/0012705700003696

in Bibtex Style

@conference{gistam24,
author={Manilo Monaco and Angela Sileo and Diana Orlandi and Maria Battagliere and Laura Candela and Mario Cimino and Gaetano Vivaldi and Vincenzo Giannico},
title={Semantic Segmentation of Crops via Hyperspectral PRISMA Satellite Images},
booktitle={Proceedings of the 10th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM},
year={2024},
pages={187-194},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012705700003696},
isbn={978-989-758-694-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM
TI - Semantic Segmentation of Crops via Hyperspectral PRISMA Satellite Images
SN - 978-989-758-694-1
AU - Monaco M.
AU - Sileo A.
AU - Orlandi D.
AU - Battagliere M.
AU - Candela L.
AU - Cimino M.
AU - Vivaldi G.
AU - Giannico V.
PY - 2024
SP - 187
EP - 194
DO - 10.5220/0012705700003696
PB - SciTePress