K-means algorithm based on stochastic distances for polarimetric synthetic aperture radar image classification

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorSeção de Engenharia Cartográfica-
Autor(es): dc.creatorNegri, Rogério Galante [UNESP]-
Autor(es): dc.creatorDa Silva, Wagner Barreto-
Autor(es): dc.creatorMendes, Tatiana Sussel Gonçalves [UNESP]-
Data de aceite: dc.date.accessioned2022-08-04T22:05:56Z-
Data de disponibilização: dc.date.available2022-08-04T22:05:56Z-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2016-10-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1117/1.JRS.10.045005-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/220713-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/220713-
Descrição: dc.descriptionThe availability of polarimetric synthetic aperture radar (PolSAR) images has increased, and consequently, the classification of such images has received immense attention. Among different classification methods in the literature, it is possible to distinguish them according to learning paradigm and approach. Unsupervised methods have as advantage the independence of labeled data for training. Regarding the approach, image classification can be performed based on its individual pixels or on previously identified regions in the image. Previous studies verified that the region-based classification of PolSAR images using stochastic distances can produce better results in comparison with the pixel-based. Faced with the independence of training data by unsupervised methods and the potential of the region-based approach with stochastic distances, this study proposes a version of the unsupervised K-means algorithm for PolSAR region-based classification based on stochastic distances. The Bhattacharyya stochastic distance between Wishart distributions was adopted to measure the dissimilarity among regions of the PolSAR image. Additionally, a measure was proposed to compare unsupervised classification results. Two case studies that consider real and simulated images were conducted, and the results showed that the proposed version of K-means achieves higher accuracy values in comparison with the classic version.-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionUniversidade Estadual Paulista Instituto de Ciência e Tecnologia Departamento de Engenharia Ambiental, Rodovia Presidente Dutra, Km 137.8-
Descrição: dc.descriptionInstituto Militar de Engenharia Seção de Engenharia Cartográfica, Praça General Tibúrcio, 80-
Descrição: dc.descriptionUniversidade Estadual Paulista Instituto de Ciência e Tecnologia Departamento de Engenharia Ambiental, Rodovia Presidente Dutra, Km 137.8-
Descrição: dc.descriptionFAPESP: 2014/14830-8-
Idioma: dc.languageen-
Relação: dc.relationJournal of Applied Remote Sensing-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectK-means-
Palavras-chave: dc.subjectpolarimetry-
Palavras-chave: dc.subjectstochastic distances-
Palavras-chave: dc.subjectunsupervised classification-
Título: dc.titleK-means algorithm based on stochastic distances for polarimetric synthetic aperture radar image classification-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

Não existem arquivos associados a este item.