A meta-methodology for improving land cover and land use classification with SAR imagery

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorNational Institute for Space Research (INPE)-
Autor(es): dc.contributorUniversidade do Estado do Rio de Janeiro (UERJ)-
Autor(es): dc.contributorPontifical Catholic University of Rio de Janeiro (PUC-Rio)-
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.creatorSoares, Marinalva Dias-
Autor(es): dc.creatorDutra, Luciano Vieira-
Autor(es): dc.creatorCosta, Gilson Alexandre Ostwald Pedro da-
Autor(es): dc.creatorFeitosa, Raul Queiroz-
Autor(es): dc.creatorNegri, Rogério Galante [UNESP]-
Autor(es): dc.creatorDiaz, Pedro M. A.-
Data de aceite: dc.date.accessioned2022-02-22T00:25:12Z-
Data de disponibilização: dc.date.available2022-02-22T00:25:12Z-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-03-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/rs12060961-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/198665-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/198665-
Descrição: dc.descriptionPer-point classification is a traditional method for remote sensing data classification, and for radar data in particular. Compared with optical data, the discriminative power of radar data is quite limited, for most applications. A way of trying to overcome these diculties is to use Region-Based Classification (RBC), also referred to as Geographical Object-Based Image Analysis (GEOBIA). RBC methods first aggregate pixels into homogeneous objects, or regions, using a segmentation procedure. Moreover, segmentation is known to be an ill-conditioned problem because it admits multiple solutions, and a small change in the input image, or segmentation parameters, may lead to significant changes in the image partitioning. In this context, this paper proposes and evaluates novel approaches for SAR data classification, which rely on specialized segmentations, and on the combination of partial maps produced by classification ensembles. Such approaches comprise a meta-methodology, in the sense that they are independent from segmentation and classification algorithms, and optimization procedures. Results are shown that improve the classification accuracy from Kappa = 0.4 (baseline method) to a Kappa = 0.77 with the presented method. Another test site presented an improvement from Kappa = 0.36 to a maximum of 0.66 also with radar data.-
Descrição: dc.descriptionImage Processing Division National Institute for Space Research (INPE)-
Descrição: dc.descriptionDepartment of Informatics and Computer Sciences Rio de Janeiro State University (UERJ)-
Descrição: dc.descriptionDepartment of Electrical Engineering Pontifical Catholic University of Rio de Janeiro (PUC-Rio)-
Descrição: dc.descriptionInstitute of Science and Technology São Paulo State University (Unesp)-
Descrição: dc.descriptionInstitute of Science and Technology São Paulo State University (Unesp)-
Idioma: dc.languageen-
Relação: dc.relationRemote Sensing-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectGEOBIA-
Palavras-chave: dc.subjectLULC classification-
Palavras-chave: dc.subjectMeta-methodologies-
Palavras-chave: dc.subjectRegion-based classification-
Palavras-chave: dc.subjectSAR classification-
Palavras-chave: dc.subjectSAR data segmentation-
Palavras-chave: dc.subjectSegmentation tuning-
Título: dc.titleA meta-methodology for improving land cover and land use classification with SAR imagery-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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