Spectral-Spatial-Aware Unsupervised Change Detection with Stochastic Distances and Support Vector Machines

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.contributorUniversidade Federal de Alagoas-
Autor(es): dc.contributorUniversidade Federal de Itajubá (UNIFEI)-
Autor(es): dc.creatorNegri, Rogerio Galante [UNESP]-
Autor(es): dc.creatorFrery, Alejandro C.-
Autor(es): dc.creatorCasaca, Wallace [UNESP]-
Autor(es): dc.creatorAzevedo, Samara-
Autor(es): dc.creatorDIas, Mauricio Araujo [UNESP]-
Autor(es): dc.creatorSilva, Erivaldo Antonio [UNESP]-
Autor(es): dc.creatorAlcantara, Enner Herenio [UNESP]-
Data de aceite: dc.date.accessioned2022-02-22T00:50:32Z-
Data de disponibilização: dc.date.available2022-02-22T00:50:32Z-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-04-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/TGRS.2020.3009483-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/207534-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/207534-
Descrição: dc.descriptionChange detection is a topic of great interest in remote sensing. A good similarity metric to compute the variations among the images is the key to high-quality change detection. However, most existing approaches rely on the fixed threshold values or the user-provided ground truth in order to be effective. The inability to deal with artificial objects such as clouds and shadows is a significant difficulty for many change-detection methods. We propose a new unsupervised change-detection framework to address those critical points. The notion of homogeneous regions is introduced together with a set of geometric operations and statistic-based criteria to characterize and distinguish formally the change and nonchange areas in a pair of remote sensing images. Moreover, a robust and statistically well-posed family of stochastic distances is also proposed, which allows comparing the probability distributions of different regions/objects in the images. These stochastic measures are then used to train a support-vector-machine-based approach in order to detect the change/nonchange areas. Three study cases using the images acquired with different sensors are given in order to compare the proposed method with other well-known unsupervised methods.-
Descrição: dc.descriptionDepartment of Environmental Engineering Sciences and Technology Institute Universidade Estadual Paulista (UNESP)-
Descrição: dc.descriptionLaboratório de Computação Científica e Análise Numérica Universidade Federal de Alagoas-
Descrição: dc.descriptionDepartment of Energy Engineering Universidade Estadual Paulista (UNESP)-
Descrição: dc.descriptionDepartment of Natural Resources Universidade Federal de Itajubá (UNIFEI)-
Descrição: dc.descriptionDepartment of Mathematics and Computer Science School of Sciences and Technology Universidade Estadual Paulista (UNESP)-
Descrição: dc.descriptionDepartment of Cartography School of Sciences and Technology Universidade Estadual Paulista (UNESP)-
Descrição: dc.descriptionDepartment of Environmental Engineering Sciences and Technology Institute Universidade Estadual Paulista (UNESP)-
Descrição: dc.descriptionDepartment of Energy Engineering Universidade Estadual Paulista (UNESP)-
Descrição: dc.descriptionDepartment of Mathematics and Computer Science School of Sciences and Technology Universidade Estadual Paulista (UNESP)-
Descrição: dc.descriptionDepartment of Cartography School of Sciences and Technology Universidade Estadual Paulista (UNESP)-
Formato: dc.format2863-2876-
Idioma: dc.languageen-
Relação: dc.relationIEEE Transactions on Geoscience and Remote Sensing-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectClassification-
Palavras-chave: dc.subjectsingle-class support vector machine (SVM)-
Palavras-chave: dc.subjectstochastic distance-
Palavras-chave: dc.subjectunsupervised change detection-
Título: dc.titleSpectral-Spatial-Aware Unsupervised Change Detection with Stochastic Distances and Support Vector Machines-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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