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Retrieving Vegetation Reradiation Patterns by Means of Artificial Neural Networks

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dc.contributor.authorGomez-Perez, Paula
dc.contributor.authorCaldeirinha, Rafael
dc.contributor.authorFernandes, Telmo R.
dc.contributor.authorCuinas, Inigo
dc.date.accessioned2025-06-20T14:12:38Z
dc.date.available2025-06-20T14:12:38Z
dc.date.issued2016
dc.description.abstractModeling vegetation usually implies obtaining experimental data by means of measurement campaigns. In general, the most accurate models need the reradiation pattern of the vegetation volume under study, or some of its related parameters. Obtaining this function might not be easy, and the measurement procedure may introduce errors depending on the location of the vegetation mass. An accurate tool to simplify this process is presented, based on the ability of artificial neural networks to infer behavioral patterns. This proposal reduces the acquisition of the experimental data to a scarce set of angles around the tree or bush, which will then be used to train a neural network capable of retrieving the desired reradiation function desired.eng
dc.description.sponsorshipThis work was supported in part by the Portuguese Government, the Portuguese Foundation for Science and Technology (FCT), the Spanish Government, the Ministerio de Economía y Competitividad under Project TEC2014-55735-C03-3R, the University of South Wales, former University of Glamorgan, Pontypridd, U.K., the AtlantTIC Research Center, and ERDF.
dc.identifier.citationP. Gómez-Pérez, R. F. S. Caldeirinha, T. R. Fernandes and I. Cuiñas, "Retrieving Vegetation Reradiation Patterns by Means of Artificial Neural Networks," in IEEE Antennas and Wireless Propagation Letters, vol. 15, pp. 1097-1100, 2016, doi: 10.1109/LAWP.2015.2493515
dc.identifier.doi10.1109/lawp.2015.2493515
dc.identifier.issn1536-1225
dc.identifier.issn1548-5757
dc.identifier.urihttp://hdl.handle.net/10400.8/13356
dc.language.isoeng
dc.peerreviewedyes
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.hasversionhttps://ieeexplore.ieee.org/document/7301995
dc.relation.ispartofIEEE Antennas and Wireless Propagation Letters
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectArtificial neural networks
dc.subjectAttenuation
dc.subjectModeling
dc.subjectPropagation
dc.subjectVegetation
dc.titleRetrieving Vegetation Reradiation Patterns by Means of Artificial Neural Networkseng
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage1100
oaire.citation.startPage1097
oaire.citation.titleIEEE Antennas and Wireless Propagation Letters
oaire.citation.volume15
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameCaldeirinha
person.familyNameFernandes
person.givenNameRafael
person.givenNameTelmo
person.identifier.ciencia-id4D18-2B1E-0960
person.identifier.ciencia-id391F-E0B5-14B5
person.identifier.orcid0000-0003-0297-7870
person.identifier.orcid0000-0003-0882-7478
person.identifier.ridF-1499-2015
person.identifier.ridB-7909-2018
person.identifier.scopus-author-id7801603527
person.identifier.scopus-author-id24779209500
relation.isAuthorOfPublicationb04f8672-d7af-443e-9104-ae8698dcdc9d
relation.isAuthorOfPublication5c77bf6a-79cb-4cfd-b316-b2ed1890bb29
relation.isAuthorOfPublication.latestForDiscoveryb04f8672-d7af-443e-9104-ae8698dcdc9d

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