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Object-oriented semantic labelling of spectral–spatial LiDAR point cloud for urban land cover classification and buildings detection / Anandakumar M. Ramiya in Geocarto international, vol 31 n° 1 - 2 (January - February 2016)  

Public [article]  inGeocarto international > vol 31 n° 1 - 2 (January - February 2016) . - pp 121 - 139 Titre : Object-oriented semantic labelling of spectral–spatial LiDAR point cloud for urban land cover classification and buildings detection Type de document : Article/Communication Auteurs : Anandakumar M. Ramiya, Auteur ; Rama Rao Nidamanuri, Auteur ; R. Krishnan, Auteur Année de publication : 2016 Article en page(s) : pp 121 - 139 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] classificateur [Termes IGN] détection de partie cachée [Termes IGN] détection du bâti [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] image multibande [Termes IGN] milieu urbain [Termes IGN] occupation du sol [Termes IGN] reconstruction 3D du bâti [Termes IGN] semis de points Résumé : (Auteur) The urban land cover mapping and automated extraction of building boundaries is a crucial step in generating three-dimensional city models. This study proposes an object-based point cloud labelling technique to semantically label light detection and ranging (LiDAR) data captured over an urban scene. Spectral data from multispectral images are also used to complement the geometrical information from LiDAR data. Initial object primitives are created using a modified colour-based region growing technique. Multiple classifier system is then applied on the features extracted from the segments for classification and also for reducing the subjectivity involved in the selection of classifier and improving the precision of the results. The proposed methodology produces two outputs: (i) urban land cover classes and (ii) buildings masks which are further reconstructed and vectorized into three-dimensional buildings footprints. Experiments carried out on three airborne LiDAR datasets show that the proposed technique successfully discriminates urban land covers and detect urban buildings. Numéro de notice : A2016-106 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2015.1034195 Date de publication en ligne : 06/05/2015 En ligne : http://www.tandfonline.com/doi/full/10.1080/10106049.2015.1034195 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80001 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2016011	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Rational function model for sensor orientation of IRS-P6 LISS-4 imagery / V. Nagasubramanian in Photogrammetric record, vol 22 n° 120 (December 2007 - February 2008)  

Public [article]  inPhotogrammetric record > vol 22 n° 120 (December 2007 - February 2008) . - pp 309 - 320 Titre : Rational function model for sensor orientation of IRS-P6 LISS-4 imagery Type de document : Article/Communication Auteurs : V. Nagasubramanian, Auteur ; P. Radhadevi, Auteur ; R. Ramachandran, Auteur ; R. Krishnan, Auteur Année de publication : 2007 Article en page(s) : pp 309 - 320 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s) [Termes IGN] géométrie de l'image [Termes IGN] géoréférencement direct [Termes IGN] image IRS-LISS [Termes IGN] modèle par fonctions rationnelles [Termes IGN] orientation du capteur [Termes IGN] point d'appui Résumé : (Auteur) This paper explores the application of a rational function model (RFM) as a replacement sensor model for IRS-P6 LISS-4 imagery. The rational polynomial coefficients (RPCs), initially generated using a rigorous sensor model (RSM) through direct georeferencing, are bias-compensated with a minimum number of ground control points and are used for various photogrammetric applications such as digital elevation model and ortho-image generation. The performance of RFM and RSM is compared in the sensor modelling of LISS-4 imagery over long strips. Results show that accuracies achieved using RFM are within 1 pixel (worst case) of the accuracies derived using RSM. Error variation as a function of the number of quasi-control points (anchor points) used for RFM fitting as well as model errors with respect to the length of the image strip are analysed. System-level accuracy does not deteriorate when the RFM is fitted up to a length of 1200 km. Absolute positioning accuracy of 1·5 pixels (~9 m) is achieved from bias-compensated RPCs. The results demonstrate the potential of RFM as a replacement sensor model. This allows standardisation of product generation packages to handle multiple sensors. Copyright RS&PS + Blackwell Publishing Numéro de notice : A2007-567 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/j.1477-9730.2007.00447.x En ligne : https://doi.org/10.1111/j.1477-9730.2007.00447.x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28930 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
106-07041		Revue	Centre de documentation	Revues en salle	Disponible