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dc.contributor.authorDel Pozo Velázquez, Javier
dc.contributor.authorChamorro Posada, Pedro 
dc.contributor.authorAguiar Pérez, Javier Manuel 
dc.contributor.authorPérez Juárez, María Ángeles 
dc.contributor.authorCasaseca de la Higuera, Juan Pablo 
dc.date.accessioned2024-01-21T20:36:48Z
dc.date.available2024-01-21T20:36:48Z
dc.date.issued2022
dc.identifier.citationFractal and Fractional, Noviembre 2022, vol. 6, n. 11. p. 657es
dc.identifier.issn2504-3110es
dc.identifier.urihttps://uvadoc.uva.es/handle/10324/64801
dc.descriptionProducción Científicaes
dc.description.abstractIdentification and monitoring of existing surface water bodies on the Earth are important in many scientific disciplines and for different industrial uses. This can be performed with the help of high-resolution satellite images that are processed afterwards using data-driven techniques to obtain the desired information. The objective of this study is to establish and validate a method to distinguish efficiently between water and land zones, i.e., an efficient method for surface water detection. In the context of this work, the method used for processing the high-resolution satellite images to detect surface water is based on image segmentation, using the Quadtree algorithm, and fractal dimension. The method was validated using high-resolution satellite images freely available at the OpenAerialMap website. The results show that, when the fractal dimensions of the tiles in which the image is divided after completing the segmentation phase are calculated, there is a clear threshold where water and land can be distinguished. The proposed scheme is particularly simple and computationally efficient compared with heavy artificial-intelligence-based methods, avoiding having any special requirements regarding the source images. Moreover, the average accuracy obtained in the case study developed for surface water detection was 96.03%, which suggests that the adopted method based on fractal dimension is able to detect surface water with a high level of accuracy.es
dc.format.mimetypeapplication/pdfes
dc.language.isoenges
dc.publisherMDPIes
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subject.classificationImage segmentationes
dc.subject.classificationFractal dimensiones
dc.subject.classificationOpenAerialMapes
dc.subject.classificationQuadtreees
dc.subject.classificationSatellite imageses
dc.subject.classificationWater detectiones
dc.titleWater detection in satellite images based on fractal dimensiones
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.holder© 2023 The authorses
dc.identifier.doi10.3390/fractalfract6110657es
dc.relation.publisherversionhttps://www.mdpi.com/2504-3110/6/11/657/htmes
dc.identifier.publicationfirstpage657es
dc.identifier.publicationissue11es
dc.identifier.publicationtitleFractal and Fractionales
dc.identifier.publicationvolume6es
dc.peerreviewedSIes
dc.description.projectEste trabajo forma parte del proyecto de investigación: PID2020-119418GB-I00 del Ministerio de Ciencia e Innovaciónes
dc.identifier.essn2504-3110es
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones
dc.subject.unesco33 Ciencias Tecnológicases
dc.subject.unesco25 Ciencias de la Tierra y del Espacioes


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