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EnglishGNSS 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].
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| Titolo: | Detecting water using UAV-based GNSS-Reflectometry data and Artificial Intelligence | |
| Autori: | ||
| Data di pubblicazione: | 2019 | |
| Abstract: | GNSS Reflectometry (GNSS-R) is a consolidated remote sensing technique that exploits the back-sca...ttered 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. | |
| ISBN: | 978-1-7281-3611-0 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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| File | Descrizione | Tipologia | Licenza | |
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| Favenza2019detecting_MetroAgriFor_NoCopyrightSign.pdf | Final Submitted version of the article. | 2. Post-print / Author's Accepted Manuscript | PUBBLICO - Tutti i diritti riservati | Visibile a tuttiVisualizza/Apri |
| Favenza2019detecting_MetroAgriFor_IEEEcopyright.pdf | Article as Published on ieeeXplore website | 2a Post-print versione editoriale / Version of Record | Non Pubblico - Accesso privato/ristretto | Administrator Richiedi una copia |
http://hdl.handle.net/11583/2799414
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