AUTOMATICALLY DETECTING TEXTUAL CONTENT IN HIGH-RESOLUTION IMAGES

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorNatural Resources Institute-
Autor(es): dc.creatorBasso, Dayara-
Autor(es): dc.creatorColnago, Marilaine-
Autor(es): dc.creatorAzevedo, Samara-
Autor(es): dc.creatorNegri, Rogério G.-
Autor(es): dc.creatorCasaca, Wallace-
Data de aceite: dc.date.accessioned2025-08-21T21:09:40Z-
Data de disponibilização: dc.date.available2025-08-21T21:09:40Z-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2020-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/IGARSS47720.2021.9553189-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/223593-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/223593-
Descrição: dc.descriptionClassifying targets in satellite images is a nontrivial task which requires dealing with a large number of undesirable elements such as clouds, building shadows and other unexpected objects. Among these, a commonly found element refers to artificially inserted post-processing objects like textual content, as the added text usually takes the form of watermarks, sensor specifications, street and place location names, etc. Manually selecting text segments is tedious, time-consuming, and requires the familiarity with image editing tools to precisely delineate these writing areas. Therefore, in this paper, a new automatic approach for detecting textual elements in satellite images is presented. Our approach combines cartoon-texture decomposition, thresholding-based rules, morphological operations, and connected component analysis into a fully automated and concise framework. Experiments on real satellite images and comparisons against well-established text detection methods demonstrate the high accuracy and low false-positive rate achieved by our approach when detecting textual content.-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionSão Paulo State University (UNESP) Dept. of Energy Engineering-
Descrição: dc.descriptionFederal University of Itajubá (UNIFEI) Natural Resources Institute-
Descrição: dc.descriptionSão Paulo State University (UNESP) Dept. of Environmental Engineering, S. J. dos Campos-
Descrição: dc.descriptionSão Paulo State University (UNESP) Dept. of Energy Engineering-
Descrição: dc.descriptionSão Paulo State University (UNESP) Dept. of Environmental Engineering, S. J. dos Campos-
Descrição: dc.descriptionFAPESP: #2013/07375-0-
Descrição: dc.descriptionFAPESP: #2018/01033-3-
Descrição: dc.descriptionFAPESP: #2018/06756-3-
Formato: dc.format4204-4207-
Idioma: dc.languageen-
Relação: dc.relationInternational Geoscience and Remote Sensing Symposium (IGARSS)-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectRemote sensing images-
Palavras-chave: dc.subjectText detection-
Título: dc.titleAUTOMATICALLY DETECTING TEXTUAL CONTENT IN HIGH-RESOLUTION IMAGES-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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