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Contribution of airborne full-waveform Lidar and image data for urban scene classification / Nesrine Chehata (07/11/2009)
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Titre : Contribution of airborne full-waveform Lidar and image data for urban scene classification Type de document : Article/Communication Auteurs : Nesrine Chehata , Auteur ; Li Guo, Auteur ; Clément Mallet
, Auteur
Editeur : New York [Etats-Unis] : IEEE Signal Processing Society Année de publication : 07/11/2009 Conférence : ICIP 2009, 16th IEEE International Conference on Image Processing 07/11/2009 10/11/2009 Le Caire Egypte Proceedings IEEE Importance : 4 p. Format : 21 x 30 cm Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image aérienne
[Termes IGN] impulsion laser
[Termes IGN] lidar à retour d'onde complète
[Termes IGN] orthoimage couleur
[Termes IGN] précision de la classification
[Termes IGN] signal lidarRésumé : (Auteur) Airborne lidar systems have become an alternative source for the acquisition of altimeter data. In addition to multi-echo laser scanner systems, full-waveform systems are able to record the whole backscattered signal for each emitted laser pulse. These data provide more information about the structure and the physical properties of the surface. This paper is focused on the classification of full-waveform lidar and airborne image data on urban scenes. Random forests are used since they provide an accurate classification and run efficiently on large datasets. Moreover, they provide measures of variable importance for each class. This is crucial to analyze the relevance of each feature for the classification of urban scenes. Random Forests provide more accurate results than Support Vector Machines with an overall accuracy of 95.75%. The most relevant features show the contribution of lidar waveforms for classifying dense urban scenes and improve the classification accuracy for all classes. Numéro de notice : C2009-047 Affiliation des auteurs : MATIS+Ext (1993-2011) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/ICIP.2009.5414234 En ligne : https://doi.org/10.1109/ICIP.2009.5414234 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=64291 Documents numériques
en open access
Contribution of airborne full-waveform Lidar ... - pdf auteurAdobe Acrobat PDF
Titre : 3D building reconstruction with parametric roof superstructures Type de document : Article/Communication Auteurs : Mathieu Brédif , Auteur ; Didier Boldo
, Auteur ; Marc Pierrot-Deseilligny
, Auteur ; Henri Maître, Auteur
Editeur : New York [Etats-Unis] : IEEE Signal Processing Society Année de publication : 2007 Conférence : ICIP 2007, 14th IEEE International Conference on Image Processing 16/09/2007 19/09/2007 San Antonio Texas - Etats-Unis Proceedings IEEE Importance : pp 1 - 4 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] détection du bâti
[Termes IGN] image aérienne
[Termes IGN] modèle numérique de surface
[Termes IGN] modélisation 3D
[Termes IGN] reconstruction 3D du bâti
[Termes IGN] toitRésumé : (auteur) This paper describes an automatic method for the detection and detailed reconstruction of 3D building models that include roof superstructures such as dormer windows or chimneys from a very high resolution Digital Elevation Model. Buildings are reconstructed as a set of roof planes with a set of parametric shapes that model roof superstructures. The proposed model-based approach minimizes a Minimum Description Length energy. Numéro de notice : C2007-045 Affiliation des auteurs : MATIS+Ext (1993-2011) Thématique : GEOMATIQUE/IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/ICIP.2007.4379211 En ligne : https://doi.org/10.1109/ICIP.2007.4379211 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101523
Titre : Tree detection in aerial LIDAR and image data Type de document : Article/Communication Auteurs : John Secord, Auteur ; Avideh Zahkor, Auteur Editeur : New York [Etats-Unis] : IEEE Signal Processing Society Année de publication : 2006 Conférence : ICIP 2006, 13th IEEE International Conference on Image Processing 08/10/2006 11/10/2006 Atlanta Géorgie - Etats-Unis Proceedings IEEE Importance : 35 p. Format : 21 x 30 cm Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage dirigé
[Termes IGN] classification dirigée
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] détection d'arbres
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] image aérienne
[Termes IGN] modélisation 3D
[Termes IGN] segmentationRésumé : (auteur) In this paper, we present an approach to detecting trees in registered aerial image and range data obtained via LiDAR. The motivation for this problem comes from automated city modeling, in which such data is used to generate textured 3-D models. Representing the trees in these models is problematic because the data is usually too sparsely sampled in tree regions to create an accurate 3-D model of the trees. Furthermore, including the tree data points interferes with the polygonization step of the building roof top models. Therefore, it is advantageous to detect and remove points that represent trees in both LiDAR and aerial imagery. In this paper, we propose a two-step method for tree detection consisting of segmentation followed by classification. The segmentation is done using a simple region-growing algorithm using weighted features from aerial image and LiDAR, such as height, texture map, height variation, and normal vector estimates. The weights for the features are determined using a learning method on random walks. The classification is done using weighted support vector machines (SVM), allowing us to control the misclassification rate. The overall problem is formulated as a binary detection problem, and receiver operating characteristic curves are shown to validate our approach. Numéro de notice : C2006-024 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Communication DOI : 10.1109/ICIP.2006.312850 En ligne : https://doi.org/10.1109/ICIP.2006.312850 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90963
Titre : Building detection by Markov object processes Type de document : Article/Communication Auteurs : Laurent Garcin, Auteur ; Xavier Descombes, Auteur ; Hervé Le Men , Auteur ; Josiane Zerubia, Auteur
Editeur : New York [Etats-Unis] : IEEE Signal Processing Society Année de publication : 2001 Conférence : ICIP 2001, 8th IEEE International Conference on Image Processing 07/10/2001 10/10/2001 Thessalonique Grèce Proceedings IEEE Importance : pp 565 - 568 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] détection du bâti
[Termes IGN] modèle de Markov
[Termes IGN] processus ponctuelRésumé : (auteur) This work aims at detecting buildings in digital aerial photographs. We model a set of buildings by a configuration of objects. We define a point process on the set of configurations, which could be divided into two parts: the first one is a prior model on the configurations which uses interactions between objects. The second one is a data model which enforces the coherence with the images. Thus we obtain a distribution /spl pi/ which has to be maximized. In order to achieve this maximum, we use a Monte Carlo Markov Chain simulation-a Metropolis-Hastings-Green algorithm-mixed with simulated annealing. Then we test this method on both synthetic and real data. Numéro de notice : C2001-058 Affiliation des auteurs : MATIS (1993-2011) Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/ICIP.2001.958555 En ligne : https://doi.org/10.1109/ICIP.2001.958555 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103063