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Classification of lidar bare-earth points, buildings, vegetation, and small objects based on region growing and angular classifier

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Classification of lidar bare-earth points, buildings, vegetation, and small objects based on region growing and angular classifier

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dc.contributor.author Sánchez Lopera, José es_ES
dc.contributor.author Lerma García, José Luis es_ES
dc.date.accessioned 2015-12-30T09:25:04Z
dc.date.available 2015-12-30T09:25:04Z
dc.date.issued 2014-10-03
dc.identifier.issn 0143-1161
dc.identifier.uri http://hdl.handle.net/10251/59300
dc.description This is an author's accepted manuscript of an article published in "International Journal of Remote Sensing" ; Volume 35, Issue 19, 2014; copyright Taylor & Francis; available online at: http://www.tandfonline.com/doi/abs/10.1080/01431161.2014.960619 es_ES
dc.description.abstract In recent years, light detection and ranging (lidar) systems have been intensively used in different urban applications such as map updating, communication analysis, virtual city modelling, risk assessment, and monitoring. A prerequisite to enhance lidar data content is to differentiate ground (bare earth) points that yield digital terrain models and off-terrain points in order to classify urban objects and vegetation. The increasing demand for a fast and efficient algorithm to extract three-dimensional urban features was the motive behind this work. A new combined approach to extract bare-earth points is proposed, and a novel methodology to automatically classify airborne laser data into different objects in an urban area is presented. In addition, a new concept of angular classification is introduced to differentiate buildings from vegetation and other small objects. The new angular classifier analyses the distribution of bare-earth points around unclassified point clusters to determine whether a cluster can be classified either as building or as vegetation. The experimental results confirm the high accuracy achieved to automatically classify urban objects in flat complex areas. es_ES
dc.language Inglés es_ES
dc.publisher Taylor & Francis: STM, Behavioural Science and Public Health Titles es_ES
dc.relation.ispartof International Journal of Remote Sensing es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject LIDAR es_ES
dc.subject Classification es_ES
dc.subject Buildings es_ES
dc.subject Vegetation es_ES
dc.subject Small objects es_ES
dc.subject Region growing es_ES
dc.subject.classification INGENIERIA CARTOGRAFICA, GEODESIA Y FOTOGRAMETRIA es_ES
dc.title Classification of lidar bare-earth points, buildings, vegetation, and small objects based on region growing and angular classifier es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1080/01431161.2014.960619
dc.rights.accessRights Cerrado 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 Sánchez Lopera, J.; Lerma García, JL. (2014). Classification of lidar bare-earth points, buildings, vegetation, and small objects based on region growing and angular classifier. International Journal of Remote Sensing. 35(19):6955-6972. doi:10.1080/01431161.2014.960619 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1080/01431161.2014.960619 es_ES
dc.description.upvformatpinicio 6955 es_ES
dc.description.upvformatpfin 6972 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 35 es_ES
dc.description.issue 19 es_ES
dc.relation.senia 281326 es_ES
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