Unsupervised Multitemporal Triclass Change Detection

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorSchool of Mathematics and Statistics-
Autor(es): dc.contributorBiomedical and Computer Engineering-
Autor(es): dc.contributorCentre of Studies in Resources Engineering (CSRE)-
Autor(es): dc.creatorNegri, Rogerio G.-
Autor(es): dc.creatorFrery, Alejandro C.-
Autor(es): dc.creatorCasaca, Wallace-
Autor(es): dc.creatorGamba, Paolo-
Autor(es): dc.creatorBhattacharya, Avik-
Data de aceite: dc.date.accessioned2025-08-21T23:37:35Z-
Data de disponibilização: dc.date.available2025-08-21T23:37:35Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2023-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/TGRS.2024.3442156-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/299147-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/299147-
Descrição: dc.descriptionChange detection is a fundamental task that involves assessing changes in a given region over multiple time periods. It has been widely applied across various fields, including monitoring deforestation, urban expansion, and natural disaster analysis. In this article, we address the critical and complex issue of automatically identifying types of changes in land cover using remotely sensed imagery. While conventional unsupervised change detection methods typically focus on comparing pairs of images and making a binary decision between 'change' and 'nonchange,' our approach tackles the challenge of analyzing long image series and identifying the kind of change. Under this condition, the unsupervised change detection process allows for a more informative identification of the land cover dynamics. Moreover, our approach transforms input data to a new representation, capturing the target's spectral response changes over time. Through the utilization of stochastic distances and an optimized thresholding scheme, areas exhibiting minimal spectral response variance are classified as unchanged, effectively distinguishing them from regions undergoing modifications. Next, by applying autocorrelation analysis, regions exhibiting temporal modifications are segregated into periodic (i.e., seasonal) and aperiodic (i.e., permanent) change cases. Experimental validation using both simulated and real-world remote sensing image series demonstrates the effectiveness of the proposed approach.-
Descrição: dc.descriptionSão Paulo State University (UNESP) Science and Technology Institute (ICT)-
Descrição: dc.descriptionVictoria University of Wellington School of Mathematics and Statistics-
Descrição: dc.descriptionInstitute of Biosciences Letters and Exact Sciences (IBILCE) São Paulo State University (UNESP)-
Descrição: dc.descriptionUniversità di Pavia Telecommunications and Remote Sensing Laboratory Department of Electrical Biomedical and Computer Engineering-
Descrição: dc.descriptionIndian Institute of Technology Bombay Centre of Studies in Resources Engineering (CSRE)-
Descrição: dc.descriptionSão Paulo State University (UNESP) Science and Technology Institute (ICT)-
Descrição: dc.descriptionInstitute of Biosciences Letters and Exact Sciences (IBILCE) São Paulo State University (UNESP)-
Idioma: dc.languageen-
Relação: dc.relationIEEE Transactions on Geoscience and Remote Sensing-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectActual data-
Palavras-chave: dc.subjectclassification-
Palavras-chave: dc.subjectfeature extraction-
Palavras-chave: dc.subjectsimulated data-
Palavras-chave: dc.subjectstatistical modeling-
Palavras-chave: dc.subjecttime series-
Título: dc.titleUnsupervised Multitemporal Triclass Change Detection-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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