GNSS Reflectometry (GNSS-R) is a consolidated remote sensing technique that exploits the back-scattered GNSS signals to retrieve information about the Earth surface. By the use of GNSS-R sensors onboard UAVs, this tool can bring significant advantages in agriculture, especially for the detection of the presence of water/moisure in specific rural areas. However, to perform this detection, GNSS-R needs a priori calibration of a threshold on the received reflected power in order to distinguish the presence/no prence of water. This is a limitation for the adoption of this approach at large scale. The work presented in this paper aims at overcoming such a limitation, proposing a novel approach based on artificial intelligence for the automatic water detection on the Earth surface, avoiding a priori, empirical, thresholding.

Detecting water using UAV-based GNSS-Reflectometry data and Artificial Intelligence / Favenza, A.; Imam, R.; Dovis, F.; Pini, M.. - ELETTRONICO. - (2019), pp. 7-12. (Intervento presentato al convegno 2019 IEEE International Workshop on Metrology for Agriculture and Forestry, MetroAgriFor 2019 tenutosi a University of Naples - Department of Agricultural Sciences, ita nel 2019) [10.1109/MetroAgriFor.2019.8909267].

Detecting water using UAV-based GNSS-Reflectometry data and Artificial Intelligence

Imam R.;Dovis F.;
2019

Abstract

GNSS Reflectometry (GNSS-R) is a consolidated remote sensing technique that exploits the back-scattered GNSS signals to retrieve information about the Earth surface. By the use of GNSS-R sensors onboard UAVs, this tool can bring significant advantages in agriculture, especially for the detection of the presence of water/moisure in specific rural areas. However, to perform this detection, GNSS-R needs a priori calibration of a threshold on the received reflected power in order to distinguish the presence/no prence of water. This is a limitation for the adoption of this approach at large scale. The work presented in this paper aims at overcoming such a limitation, proposing a novel approach based on artificial intelligence for the automatic water detection on the Earth surface, avoiding a priori, empirical, thresholding.
2019
978-1-7281-3611-0
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2799414