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A Non-parametric Approach for Accurate Contextual Classification of LIDAR and Imagery Data Fusion

 

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dc.creator García Gutiérrez, Jorge es
dc.creator Mateos García, Daniel es
dc.creator Riquelme Santos, José Cristóbal es
dc.date.accessioned 2016-06-23T07:21:30Z
dc.date.available 2016-06-23T07:21:30Z
dc.date.issued 2012
dc.identifier.isbn 978-3-642-28930-9 es
dc.identifier.issn 0302-9743 es
dc.identifier.uri http://hdl.handle.net/11441/42648
dc.description.abstract Light Detection and Ranging (LIDAR) has become a very important tool to many environmental applications. This work proposes to use LIDAR and image data fusion to develop high-resolution thematic maps. A novel methodology is presented which starts building a matrix of statistics from spectral and spatial information by feature extraction on the available bands (RGB from images, and intensity and height from LIDAR). Then, a contextual classification is applied to generate the final map using a support vector machine (SVM) to classify every cell and the nearest neighbor (NN) rule to sequentially reclassify each cell. The results obtained by this novel method, called SVMNNS (SVM and NN Stacking), are compared with non-contextual and contextual SVMs. It is shown that SVMNNS obtains the best results when applied to real data from the Iberian peninsula. es
dc.format application/pdf es
dc.language.iso eng es
dc.publisher Springer es
dc.relation.ispartof Hybrid Artificial Intelligent Systems :7th International Conference, HAIS 2012, Salamanca, Spain, March 28-30th, 2012. Proceedings, Part II. Lecture Notes in Computer Science, v.7209 es
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 Internacional *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/ *
dc.subject remote sensing es
dc.subject supervised learning es
dc.subject contextual classifiers es
dc.title A Non-parametric Approach for Accurate Contextual Classification of LIDAR and Imagery Data Fusion es
dc.type info:eu-repo/semantics/bookPart es
dc.type.version info:eu-repo/semantics/acceptedVersion es
dc.rights.accessrights info:eu-repo/semantics/openAccess es
dc.contributor.affiliation Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos es
dc.identifier.doi http://dx.doi.org/10.1007/978-3-642-28931-6_44 es
idus.format.extent 12 es
dc.publication.initialPage 455 es
dc.publication.endPage 466 es
dc.relation.publicationplace Berlin es
dc.identifier.idus https://idus.us.es/xmlui/handle/11441/42648
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