Land use image classification through optimum-path forest clustering

dc.contributorUniversidade Estadual Paulista (UNESP)
dc.creatorPisani, R.
dc.creatorRiedel, P.
dc.creatorFerreira, M.
dc.creatorMarques, M.
dc.creatorMizobe, R.
dc.creatorPapa, J.
dc.date2014-05-27T11:26:07Z
dc.date2016-10-25T18:35:28Z
dc.date2014-05-27T11:26:07Z
dc.date2016-10-25T18:35:28Z
dc.date2011-11-16
dc.date.accessioned2017-04-06T01:53:37Z
dc.date.available2017-04-06T01:53:37Z
dc.identifierInternational Geoscience and Remote Sensing Symposium (IGARSS), p. 826-829.
dc.identifierhttp://hdl.handle.net/11449/72802
dc.identifierhttp://acervodigital.unesp.br/handle/11449/72802
dc.identifier10.1109/IGARSS.2011.6049258
dc.identifierWOS:000297496300199
dc.identifier2-s2.0-80955164075
dc.identifierhttp://dx.doi.org/10.1109/IGARSS.2011.6049258
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/893639
dc.descriptionLand use classification has been paramount in the last years, since we can identify illegal land use and also to monitor deforesting areas. Although one can find several research works in the literature that address this problem, we propose here the land use recognition by means of Optimum-Path Forest Clustering (OPF), which has never been applied to this context up to date. Experiments among Optimum-Path Forest, Mean Shift and K-Means demonstrated the robustness of OPF for automatic land use classification of images obtained by CBERS-2B and Ikonos-2 satellites. © 2011 IEEE.
dc.languageeng
dc.relationInternational Geoscience and Remote Sensing Symposium (IGARSS)
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectLand use
dc.subjectmean shift
dc.subjectoptimum-path forest
dc.subjectunsupervised classification
dc.subjectK-means
dc.subjectLanduse classifications
dc.subjectMean shift
dc.subjectUnsupervised classification
dc.subjectGeology
dc.subjectRemote sensing
dc.titleLand use image classification through optimum-path forest clustering
dc.typeOtro


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