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Relevance of airborne lidar and multispectral image data for urban scene classification using random forests / Li Guo in ISPRS Journal of photogrammetry and remote sensing, vol 66 n° 1 (January - February 2011)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 66 n° 1 (January - February 2011) . - pp 56 - 66 Titre : Relevance of airborne lidar and multispectral image data for urban scene classification using random forests Type de document : Article/Communication Auteurs : Li Guo, Auteur ; Nesrine Chehata , Auteur ; Clément Mallet , Auteur ; Samia Boukir, Auteur Année de publication : 2011 Article en page(s) : pp 56 - 66 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] analyse discriminante [Termes IGN] classification dirigée [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données lidar [Termes IGN] écho multiple [Termes IGN] forme d'onde pleine [Termes IGN] image multibande [Termes IGN] semis de points [Termes IGN] zone urbaine dense Résumé : (Auteur) Airborne lidar systems have become a source for the acquisition of elevation data. They provide georeferenced, irregularly distributed 3D point clouds of high altimetric accuracy. Moreover, these systems can provide for a single laser pulse, multiple returns or echoes, which correspond to different illuminated objects. In addition to multi-echo laser scanners, full-waveform systems are able to record 1D signals representing a train of echoes caused by reflections at different targets. These systems provide more information about the structure and the physical characteristics of the targets. Many approaches have been developed, for urban mapping, based on aerial lidar solely or combined with multispectral image data. However, they have not assessed the importance of input features. In this paper, we focus on a multi-source framework using aerial lidar (multi-echo and full waveform) and aerial multispectral image data. We aim to study the feature relevance for dense urban scenes. The Random Forests algorithm is chosen as a classifier: it runs efficiently on large datasets, and provides measures of feature importance for each class. The margin theory is used as a confidence measure of the classifier, and to confirm the relevance of input features for urban classification. The quantitative results confirm the importance of the joint use of optical multispectral and lidar data. Moreover, the relevance of full-waveform lidar features is demonstrated for building and vegetation area discrimination. Numéro de notice : A2011-016 Affiliation des auteurs : IGN+Ext (1940-2011) Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2010.08.007 Date de publication en ligne : 22/09/2010 En ligne : https://doi.org/10.1016/j.isprsjprs.2010.08.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30798 [article]

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Support vectors selection for supervised learning using an ensemble approach / Li Guo (2010)  

Public Titre : Support vectors selection for supervised learning using an ensemble approach Type de document : Article/Communication Auteurs : Li Guo, Auteur ; Samia Boukir, Auteur ; Nesrine Chehata , Auteur Editeur : New-York : IEEE Computer society Année de publication : 2010 Conférence : ICPR 2010, 20th IAPR International Conference on Pattern Recognition 23/08/2010 26/08/2010 Istanbul Turquie Proceedings IEEE Importance : pp 37 - 40 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] théorie des ensembles Résumé : (auteur) Support Vector Machines (SVMs) are popular for pattern classification. However, training a SVM requires large memory and high processing time, especially for large datasets, which limits their applications. To speed up their training, we present a new efficient support vector selection method based on ensemble margin, a key concept in ensemble classifiers. This algorithm exploits a new version of the margin of an ensemble-based classification and selects the smallest margin instances as support vectors. Our experimental results show that our method reduces training set size significantly without degrading the performance of the resulting SVMs classifiers. Numéro de notice : C2010-073 Affiliation des auteurs : MATIS+Ext (1993-2011) Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/ICPR.2010.18 En ligne : https://doi.ieeecomputersociety.org/10.1109/ICPR.2010.18 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102056

A two-pass random forests classification of airborne Lidar and image data on urban scenes / Li Guo (2010)  

Public Titre : A two-pass random forests classification of airborne Lidar and image data on urban scenes Type de document : Article/Communication Auteurs : Li Guo, Auteur ; Nesrine Chehata , Auteur ; Samia Boukir, Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2010 Conférence : ICIP 2010, 17th IEEE International Conference on Image Processing 25/09/2010 29/09/2010 Hong Kong Turquie Proceedings IEEE Importance : pp 1369 - 1372 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] image RVB [Termes IGN] intervalle de classe [Termes IGN] scène urbaine Résumé : (auteur) Random forests ensemble classifier showed to be suitable for classifying multisource data such as lidar and RGB image for urban scene mapping. However, two major problems remain: (1) the class boundaries are not well classified, a common issue in classification (2) the data are highly imbalanced raising another issue more specific to urban scenes. In this paper, we propose a new ensemble method based on the margin paradigm to improve the classification accuracy of minor classes. Random forests classifier is used in a two-pass methodology with an improved capability for classifying imbalanced data. Numéro de notice : C2010-061 Affiliation des auteurs : MATIS+Ext (1993-2011) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/ICIP.2010.5653030 Date de publication en ligne : 03/12/2010 En ligne : https://doi.org/10.1109/ICIP.2010.5653030 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101943

Contribution of airborne full-waveform Lidar and image data for urban scene classification / Nesrine Chehata (07/11/2009)    

Public 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 lidar Résumé : (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

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Airborne lidar feature selection for urban classification using random forests / Nesrine Chehata (2009)  

Public contenu dans ISPRS Workshop Laserscanning'09, Paris, France, September 1-2, 2009 / Frédéric Bretar (2009)   Titre : Airborne lidar feature selection for urban classification using random forests Type de document : Article/Communication Auteurs : Nesrine Chehata , Auteur ; Li Guo, Auteur ; Clément Mallet , Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2009 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 38-3/W8 Conférence : ISPRS 2009, Workshop LaserScanning 01/09/2009 02/09/2009 Paris France OA Archives proceedings Importance : pp 207 - 212 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] classification dirigée [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données lidar [Termes IGN] lidar à retour d'onde complète Résumé : (Auteur) Various multi-echo and Full-waveform (FW) lidar features can be processed. In this paper, multiple classifers are applied to lidar feature selection for urban scene classification. Random forests are used since they provide an accurate classification and run efficiently on large datasets. Moreover, they return measures of variable importance for each class. The feature selection is obtained by backward elimination of features depending on their importance. This is crucial to analyze the relevance of each lidar feature for the classification of urban scenes. The Random Forests classification using selected variables provide an overall accuracy of 94.35%. Numéro de notice : C2009-005 Affiliation des auteurs : MATIS+Ext (1993-2011) Thématique : FORET/IMAGERIE Nature : Communication DOI : sans En ligne : https://www.isprs.org/proceedings/XXXVIII/3-W8/papers/207_laserscanning09.pdf Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=65045