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Evaluation of automatic building detection approaches combining high resolution images and LiDAR data

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Evaluation of automatic building detection approaches combining high resolution images and LiDAR data

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dc.contributor.author Hermosilla, T. es_ES
dc.contributor.author Ruiz Fernández, Luis Ángel es_ES
dc.contributor.author Recio Recio, Jorge Abel es_ES
dc.contributor.author Estornell Cremades, Javier es_ES
dc.date.accessioned 2013-05-14T14:38:19Z
dc.date.available 2013-05-14T14:38:19Z
dc.date.issued 2011
dc.identifier.issn 2072-4292
dc.identifier.uri http://hdl.handle.net/10251/28845
dc.description.abstract In this paper, two main approaches for automatic building detection and localization using high spatial resolution imagery and LiDAR data are compared and evaluated: thresholding-based and object-based classification. The thresholding-based approach is founded on the establishment of two threshold values: one refers to the minimum height to be considered as building, defined using the LiDAR data, and the other refers to the presence of vegetation, which is defined according to the spectral response. The other approach follows the standard scheme of object-based image classification: segmentation, feature extraction and selection, and classification, here performed using decision trees. In addition, the effect of the inclusion in the building detection process of contextual relations with the shadows is evaluated. Quality assessment is performed at two different levels: area and object. Area-level evaluates the building delineation performance, whereas object-level assesses the accuracy in the spatial location of individual buildings. The results obtained show a high efficiency of the evaluated methods for building detection techniques, in particular the thresholding-based approach, when the parameters are properly adjusted and adapted to the type of urban landscape considered. © 2011 by the authors. es_ES
dc.description.sponsorship The authors appreciate the financial support provided by the Spanish Ministry of Science and Innovation and FEDER in the framework of the projects CGL2009-14220 and CGL2010-19591/BTE, and the support of the Spanish Instituto Geografico Nacional (IGN). en_EN
dc.language Inglés es_ES
dc.publisher MDPI es_ES
dc.relation.ispartof Remote Sensing es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Building detection es_ES
dc.subject High spatial resolution imagery es_ES
dc.subject LiDAR es_ES
dc.subject Object-based image classification es_ES
dc.subject Automatic building detection es_ES
dc.subject Feature extraction and selection es_ES
dc.subject High efficiency es_ES
dc.subject High resolution image es_ES
dc.subject LIDAR data es_ES
dc.subject Object based es_ES
dc.subject Quality assessment es_ES
dc.subject Spatial location es_ES
dc.subject Spectral response es_ES
dc.subject Urban landscape es_ES
dc.subject Decision trees es_ES
dc.subject Feature extraction es_ES
dc.subject Image analysis es_ES
dc.subject Image classification es_ES
dc.subject Image resolution es_ES
dc.subject Image segmentation es_ES
dc.subject Optical radar es_ES
dc.subject Quality control es_ES
dc.subject Rating es_ES
dc.subject Buildings es_ES
dc.subject.classification INGENIERIA CARTOGRAFICA, GEODESIA Y FOTOGRAMETRIA es_ES
dc.title Evaluation of automatic building detection approaches combining high resolution images and LiDAR data es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/rs3061188
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//CGL2009-14220-C02-01/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//CGL2010-19591/ES/DESARROLLO DE METODOLOGIAS INTEGRADAS PARA LA ACTUALIZACION DE BASES DE DATOS DE OCUPACION DEL SUELO/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Cartográfica Geodesia y Fotogrametría - Departament d'Enginyeria Cartogràfica, Geodèsia i Fotogrametria es_ES
dc.description.bibliographicCitation Hermosilla, T.; Ruiz Fernández, LÁ.; Recio Recio, JA.; Estornell Cremades, J. (2011). Evaluation of automatic building detection approaches combining high resolution images and LiDAR data. Remote Sensing. 3:1188-1210. https://doi.org/10.3390/rs3061188 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://www.mdpi.com/2072-4292/3/6/1188/pdf es_ES
dc.description.upvformatpinicio 1188 es_ES
dc.description.upvformatpfin 1210 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 3 es_ES
dc.relation.senia 193523
dc.contributor.funder Ministerio de Ciencia e Innovación es_ES
dc.contributor.funder Instituto Geográfico Nacional es_ES
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