Integration of Multiclass Strategies and Different Kernel Functions into Support Vector Machines for Remote Sensing Image Classification

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorMaselli, Luccas Zambon-
Autor(es): dc.creatorNegri, Rogério Galante-
Data de aceite: dc.date.accessioned2025-08-21T22:23:22Z-
Data de disponibilização: dc.date.available2025-08-21T22:23:22Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2019-01-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.14393/rbcv71n1-47208-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/246929-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/246929-
Descrição: dc.descriptionAlthough several image classification methods have been proposed in literature, Support Vector Machine (SVM) is widely used in Remote Sensing applications. In addition to its robust mathematical formulation, the possibility of using different kernel functions and multiclass strategies highlights the attractiveness of this method. While kernel functions make possible to enhance the classification performance face to non-linearly separable data, multiclass strategies extend the original formulation of SVM in order to cope with problems involving more than two classes. However, it worth mention that particular choice involving a kernel function and a multiclass strategy implies directly on the classification performance. Furthermore, the best choice may be not a simple task. In order to reduce the freedom degree that arises from different possible combinations between kernel function and multiclass strategy, two architectures to training SVM are proposed. Three case studies involving land use and land cover classification with images acquired by different sensors are carried in order to verify the potential of presented architectures in comparison to usual approaches.-
Descrição: dc.descriptionUniversidade Estadual “Júlio de Mesquita Filho” - UNESP Instituto de Ciência e Tecnologia - ICT Departamento de Engenharia Ambiental-
Descrição: dc.descriptionUniversidade Estadual “Júlio de Mesquita Filho” - UNESP Instituto de Ciência e Tecnologia - ICT Departamento de Engenharia Ambiental-
Formato: dc.format149-175-
Idioma: dc.languagept_BR-
Relação: dc.relationRevista Brasileira de Cartografia-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectImage Classification-
Palavras-chave: dc.subjectKernel Functions-
Palavras-chave: dc.subjectMulticlass strategy-
Palavras-chave: dc.subjectSupport Vector Machines-
Título: dc.titleIntegration of Multiclass Strategies and Different Kernel Functions into Support Vector Machines for Remote Sensing Image Classification-
Título: dc.titleIntegração entre Estratégias Multiclasses e diferentes Funções Kernel em Máquinas de Vetores Suporte para Classificação de Imagens de Sensoriamento Remoto-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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