LAND-USE CLASSIFICATION USING FINITE ELEMENT MACHINES

dc.contributorUniversidade Estadual Paulista (Unesp)
dc.contributorFac Southwest Paulista
dc.contributorUniv Jose do Rosario Vellano
dc.date.accessioned2019-10-03T18:20:08Z
dc.date.accessioned2022-12-19T17:51:18Z
dc.date.available2019-10-03T18:20:08Z
dc.date.available2022-12-19T17:51:18Z
dc.date.created2019-10-03T18:20:08Z
dc.date.issued2018-01-01
dc.identifierIgarss 2018 - 2018 Ieee International Geoscience And Remote Sensing Symposium. New York: Ieee, p. 7316-7319, 2018.
dc.identifier2153-6996
dc.identifierhttp://hdl.handle.net/11449/184129
dc.identifierWOS:000451039807004
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5365185
dc.description.abstractSatellite images have been used in a number of applications, both in the academy and in the industry. One critical purpose concerns the land-use classification, which aims at automatically identifying different land-use applications, which range from economy and environmental monitoring to resources planning. In this paper, we introduce a new machine learning technique called Finite Element Machines (FEMa) in the context of land-use classification using satellite images. We show that FEMa can obtain results that are comparable to some state-of-the-art techniques in the literature.
dc.languageeng
dc.publisherIeee
dc.relationIgarss 2018 - 2018 Ieee International Geoscience And Remote Sensing Symposium
dc.rightsAcesso aberto
dc.sourceWeb of Science
dc.subjectLand-use classification
dc.subjectFinite Element Machines
dc.subjectRemote Sensing
dc.titleLAND-USE CLASSIFICATION USING FINITE ELEMENT MACHINES
dc.typeActas de congresos


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