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PpC: a new method to reduce the density of lidar data. Does it affect the DEM accuracy? / Sandra Bujan in Photogrammetric record, vol 34 n° 167 (September 2019)  

Public [article]  inPhotogrammetric record > vol 34 n° 167 (September 2019) . - pp Titre : PpC: a new method to reduce the density of lidar data. Does it affect the DEM accuracy? Type de document : Article/Communication Auteurs : Sandra Bujan, Auteur ; Edouardo M. González‐Ferreiro, Auteur ; Miguel Cordero, Auteur ; David Miranda, Auteur Année de publication : 2019 Article en page(s) : pp Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] densité des points [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] échantillonnage [Termes IGN] modèle numérique de surface [Termes IGN] pente [Termes IGN] précision [Termes IGN] R (langage) [Termes IGN] réduction Résumé : (auteur) In cost–benefit analysis of lidar data acquisition, point density is often artificially reduced in order to examine how this affects the quality of derived products. However, the performance of the different density reduction methods has not yet been compared and their influence on the accuracy of the models and results has not been evaluated. A novel method for reducing the point density, termed Proportional per Cell (PpC), is presented and compared with the performance of three other reduction methods, examining their influence on the accuracy of lidar‐derived digital surface models using ISPRS reference data. The results indicate that the PpC method was better at conserving the characteristics of the original data. However, point density, sample type and slope had a greater influence than the reduction method used. Numéro de notice : A2019-499 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/phor.12295 Date de publication en ligne : 10/10/2019 En ligne : https://doi.org/10.1111/phor.12295 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93763 [article]

Land use classification from lidar data and ortho-images in a rural area / Sandra Bujan in Photogrammetric record, vol 27 n° 140 (December 2012 - February 2013)  

Public [article]  inPhotogrammetric record > vol 27 n° 140 (December 2012 - February 2013) . - pp 401 - 422 Titre : Land use classification from lidar data and ortho-images in a rural area Type de document : Article/Communication Auteurs : Sandra Bujan, Auteur ; Edouardo M. González‐Ferreiro, Auteur ; et al., Auteur Année de publication : 2012 Article en page(s) : pp 401 - 422 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse comparative [Termes IGN] classification orientée objet [Termes IGN] détection du bâti [Termes IGN] données lidar [Termes IGN] image aérienne [Termes IGN] occupation du sol [Termes IGN] paysage rural [Termes IGN] précision des données [Termes IGN] superposition de données Résumé : (Auteur) Obtaining information on the distribution of rural landscape types is an active research topic within Spanish rural studies. This paper presents a new hierarchical object-based classification method for the automatic detection of various land use classes in a rural area, combining lidar data and aerial images. In view of the upcoming availability of low-density lidar data (0·5 pulses/m2) for most of the territory of Spain, this paper assesses the feasibility and accuracy of the proposed method for various lidar data densities. Such an assessment was conducted using two approaches: firstly, based on the final classification, which produced an overall accuracy over 96% and a kappa index above 0·95 for the combinations of the aerial image and lidar data-sets with four different densities; and secondly, based solely on the areas classified as buildings. In the second approach, the accuracy of the classification for building detection at pixel and object level was assessed. The object-oriented classification of buildings produced an index of correctness of over 99% and an index of completeness of about 95%. The results reveal a high agreement between classification and ground truth data. Numéro de notice : A2012-624 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/j.1477-9730.2012.00698.x Date de publication en ligne : 18/12/2012 En ligne : https://doi.org/10.1111/j.1477-9730.2012.00698.x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32070 [article]

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