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The EuroSDR project "radiometric aspects of digital photogrammetric images" : results of the empirical phase / Eija Honkavaara (01/06/2011)  

Public Titre : The EuroSDR project "radiometric aspects of digital photogrammetric images" : results of the empirical phase Type de document : Article/Communication Auteurs : Eija Honkavaara, Auteur ; Roman Arbiol, Auteur ; Lauri Markelin, Auteur ; Lucas Martinez, Auteur ; Mathieu Brédif , Auteur ; Laure Chandelier , Auteur ; Lâmân Lelégard , Auteur ; et al., Auteur Editeur : Helsinki : Finnish Geodetic Institute FGI Année de publication : 01/06/2011 Conférence : ISPRS 2011, High-Resolution Earth Imaging for Geospatial Information workshop 14/06/2011 17/06/2011 Hanovre Allemagne OA ISPRS Archives Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s) [Termes IGN] ADS40 [Termes IGN] DMC [Termes IGN] étalonnage de capteur (imagerie) [Termes IGN] étalonnage géométrique [Termes IGN] étalonnage radiométrique [Termes IGN] réflectance [Termes IGN] uniformisation d'histogramme Résumé : (Auteur) This article presents the empirical research carried out in the context of the multi-site EuroSDR project “Radiometric aspects of digital photogrammetric images” and provides highlights of the results. The investigations have considered the vicarious radiometric and spatial resolution validation and calibration of the sensor system, radiometric processing of the image blocks either by performing relative radiometric block equalization or into absolutely reflectance calibrated products, and finally aspects of practical applications on NDVI layer generation and tree species classification. The data sets were provided by Leica Geosystems ADS40 and Intergraph DMC and the participants represented stakeholders in National Mapping Authorities, software development and research. The investigations proved the stability and quality of evaluated imaging systems with respect to radiometry and optical system. The first new-generation methods for reflectance calibration and equalization of photogrammetric image block data provided promising accuracy and were also functional from the productivity and usability points of view. The reflectance calibration methods provided up to 5% accuracy without any ground reference. Application oriented results indicated that automatic interpretation methods will benefit from the optimal use of radiometrically accurate multi-view photogrammetric imagery. Numéro de notice : C2011-054 Affiliation des auteurs : MATIS+Ext (1993-2011) Thématique : IMAGERIE Nature : Communication DOI : 10.5194/isprsarchives-XXXVIII-4-W19-123-2011 Date de publication en ligne : 07/09/2012 En ligne : http://dx.doi.org/10.5194/isprsarchives-XXXVIII-4-W19-123-2011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=64373

Conditional random fields for the classification of LiDAR point clouds / Joachim Niemeyer (2011)  

Public Titre : Conditional random fields for the classification of LiDAR point clouds Type de document : Article/Communication Auteurs : Joachim Niemeyer, Auteur ; Clément Mallet , Auteur ; Franz Rottensteiner, Auteur ; Uwe Soergel, Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2011 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 38/4-W19 Conférence : ISPRS 2011, High-Resolution Earth Imaging for Geospatial Information workshop 14/06/2011 17/06/2011 Hanovre Allemagne OA ISPRS Archives Importance : 6 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] champ aléatoire conditionnel [Termes IGN] densité des points [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] forme d'onde pleine [Termes IGN] prise en compte du contexte [Termes IGN] semis de points [Termes IGN] zone urbaine dense Résumé : (auteur) In this paper we propose a probabilistic supervised classification algorithm for LiDAR (Light Detection And Ranging) point clouds. Several object classes (i.e. ground, building and vegetation) can be separated reliably by considering each point's neighbourhood. Based on Conditional Random Fields (CRF) this contextual information can be incorporated into classification process in order to improve results. Since we want to perform a point-wise classification, no primarily segmentation is needed. Therefore, each 3D point is regarded as a graph's node, whereas edges represent links to the nearest neighbours. Both nodes and edges are associated with features and have effect on the classification. We use some features available from full waveform technology such as amplitude, echo width and number of echoes as well as some extracted geometrical features. The aim of the paper is to describe the CRF model set-up for irregular point clouds, present the features used for classification, and to discuss some results. The resulting overall accuracy is about 94 %. Numéro de notice : C2011-069 Affiliation des auteurs : MATIS+Ext (1993-2011) Thématique : IMAGERIE Nature : Communication DOI : 10.5194/isprsarchives-XXXVIII-4-W19-209-2011 Date de publication en ligne : 07/09/2012 En ligne : https://doi.org/10.5194/isprsarchives-XXXVIII-4-W19-209-2011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101398