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Multisource classification using Support Vector Machines: an empirical comparison with Decision Tree and Neural Network classifiers / P. Watanachaturaporn in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 2 (February 2008)
[article]
Titre : Multisource classification using Support Vector Machines: an empirical comparison with Decision Tree and Neural Network classifiers Type de document : Article/Communication Auteurs : P. Watanachaturaporn, Auteur ; M. Arora, Auteur ; K. Varshney, Auteur Année de publication : 2008 Article en page(s) : pp 239 - 246 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse comparative
[Termes IGN] classification par arbre de décision
[Termes IGN] classification par réseau neuronal
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données multisources
[Termes IGN] extraction automatique
[Termes IGN] Himalaya
[Termes IGN] image IRS-LISS
[Termes IGN] Kappa de Cohen
[Termes IGN] modèle numérique de surface
[Termes IGN] occupation du solRésumé : (Auteur) Remote sensing image classification has proven to be attractive for extracting useful thematic information such as landcover. However, often for a given application, spectral information acquired by a remote sensing sensor may not be sufficient to derive accurate information. Incorporation of data from other sources such as a digital elevation model (DEM), and geophysical and geological data may assist in achieving more accurate land-cover classification from remote sensing images. Recently, support vector machines (SVM) have been proposed as an alternative for classification of remote sensing data, and the results are promising. In this paper, we employ the SVM algorithm to perform multisource classification. An IRS–1C LISS III image along with normalized differenced vegetation index (NDVI) image and DEM are used to produce a land-cover classification for a region in the Himalayas. The accuracy of SVM-based multisource classification is compared with several other nonparametric algorithms namely a decision tree classifier, and back propagation and radial basis function neural network classifiers. The well-known kappa coefficient of agreement is used to assess classification accuracy. The differences in the kappa coefficient of classifiers have been statistically evaluated using a pairwise Z-test. The results show a significant increase in the accuracy of the SVM based classifier on incorporation of ancillary data over classification performed solely on the basis of spectral data from remote sensing sensors. Copyright ASPRS Numéro de notice : A2008-048 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.74.2.239 En ligne : https://doi.org/10.14358/PERS.74.2.239 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29043
in Photogrammetric Engineering & Remote Sensing, PERS > vol 74 n° 2 (February 2008) . - pp 239 - 246[article]Multispectral land use classification using neural networks and support vector machines: one or the other, or both? / B. Dixon in International Journal of Remote Sensing IJRS, vol 29 n°3-4 (February 2008)
[article]
Titre : Multispectral land use classification using neural networks and support vector machines: one or the other, or both? Type de document : Article/Communication Auteurs : B. Dixon, Auteur ; N. Candade, Auteur Année de publication : 2008 Article en page(s) : pp 1185 - 1206 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse comparative
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] classification par réseau neuronal
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] occupation du solRésumé : (Auteur) Land use classification is an important part of many remote sensing applications. A lot of research has gone into the application of statistical and neural network classifiers to remote-sensing images. This research involves the study and implementation of a new pattern recognition technique introduced within the framework of statistical learning theory called Support Vector Machines (SVMs), and its application to remote-sensing image classification. Standard classifiers such as Artificial Neural Network (ANN) need a number of training samples that exponentially increase with the dimension of the input feature space. With a limited number of training samples, the classification rate thus decreases as the dimensionality increases. SVMs are independent of the dimensionality of feature space as the main idea behind this classification technique is to separate the classes with a surface that maximizes the margin between them, using boundary pixels to create the decision surface. Results from SVMs are compared with traditional Maximum Likelihood Classification (MLC) and an ANN classifier. The findings suggest that the ANN and SVM classifiers perform better than the traditional MLC. The SVM and the ANN show comparable results. However, accuracy is dependent on factors such as the number of hidden nodes (in the case of ANN) and kernel parameters (in the case of SVM). The training time taken by the SVM is several magnitudes less. Copyright Taylor & Francis Numéro de notice : A2008-009 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160701294661 En ligne : https://doi.org/10.1080/01431160701294661 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29004
in International Journal of Remote Sensing IJRS > vol 29 n°3-4 (February 2008) . - pp 1185 - 1206[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 080-08021 RAB Revue Centre de documentation En réserve L003 Disponible The application of artificial neural networks to the analysis of remotely sensed data / J.F. Mas in International Journal of Remote Sensing IJRS, vol 29 n°3-4 (February 2008)
[article]
Titre : The application of artificial neural networks to the analysis of remotely sensed data Type de document : Article/Communication Auteurs : J.F. Mas, Auteur ; J.J. Flores, Auteur Année de publication : 2008 Article en page(s) : pp 617 - 663 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage automatique
[Termes IGN] classification par réseau neuronal
[Termes IGN] image aérienne
[Termes IGN] image satellite
[Termes IGN] réseau neuronal artificielRésumé : (Auteur) Artificial neural networks (ANNs) have become a popular tool in the analysis of remotely sensed data. Although significant progress has been made in image classification based upon neural networks, a number of issues remain to be resolved. This paper reviews remotely sensed data analysis with neural networks. First, we present an overview of the main concepts underlying ANNs, including the main architectures and learning algorithms. Then, the main tasks that involve ANNs in remote sensing are described. The limitations and crucial issues relating to the application of the neural network approach are discussed. A brief review of the implementation of ANNs in some of the most popular image processing software packages is presented. Finally, we discuss the application perspectives of neural networks in remote sensing image analysis. Copyright Taylor & Francis Numéro de notice : A2008-004 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160701352154 En ligne : https://doi.org/10.1080/01431160701352154 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28999
in International Journal of Remote Sensing IJRS > vol 29 n°3-4 (February 2008) . - pp 617 - 663[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 080-08021 RAB Revue Centre de documentation En réserve L003 Disponible Analyse et traitement d'ondes Lidar pour la cartographie et la reconnaissance de formes : application au milieu urbain / Clément Mallet (2008)
Titre : Analyse et traitement d'ondes Lidar pour la cartographie et la reconnaissance de formes : application au milieu urbain Titre original : Lidar waveform analysis and processing for cartography and pattern recognition: application to urban areas Type de document : Article/Communication Auteurs : Clément Mallet , Auteur ; Adrien Chauve , Auteur ; Frédéric Bretar, Auteur Editeur : Orsay, Chambéry : Association Française de l'Intelligence Artificielle AFIA Année de publication : 2008 Conférence : RFIA 2008, 16e conférence Reconnaissance des Formes et Intelligence Artificielle 22/01/2008 25/01/2008 Amiens France Importance : pp 693 - 702 Format : 21 x 30 cm Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] classification dirigée
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forme d'onde
[Termes IGN] milieu urbain
[Termes IGN] onde électromagnétique
[Termes IGN] reconnaissance de formes
[Termes IGN] segmentation
[Termes IGN] semis de points
[Termes IGN] signal laserRésumé : (Auteur) Toute onde lidar rétrodiffusée par la surface terrestre contient des informations sur les cibles atteintes ayant contribué à la forme de l’onde. Les systèmes lidar capables de numériser l’intégralité des signaux retour sont apparus récemment et permettent le traitement a posteriori de ces profils altimétriques. Nous présentons dans cet article une méthode d’analyse puis de traitement des ondes lidar dans un contexte de cartographie automatique. Tout d’abord, nous montrons que l’analyse fine des ondes permet une densification des nuages de points 3D. Dans un second temps, le traitement a posteriori des signaux conduit à leur modélisation sous forme paramétrique. Nous proposons alors une méthode de reconnaissance de formes appliquée au milieu urbain. Une classification supervisée par Séparateurs à Vaste Marge est ainsi employée pour prendre en compte les caractéristiques des échos extraits lors de la phase de traitement. Les résultats montrent que la segmentation d’une zone urbaine en classes bâti, végétation, sol naturel et sol artificiel est possible à partir des ondes lidar seulement. Numéro de notice : 13576 Affiliation des auteurs : MATIS (1993-2011) Thématique : IMAGERIE Nature : Communication DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=64263 Documents numériques
en open access
13576_art_analyse_ondes_lidar_mallet.pdfAdobe Acrobat PDF
Titre : Analysis of full-waveform Lidar data for classification of urban areas Type de document : Article/Communication Auteurs : Uwe Soergel, Auteur ; Frédéric Bretar, Auteur ; Clément Mallet , Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2008 Collection : International Archives of Photogrammetry and Remote Sensing, ISSN 0252-8231 num. 37-B3 Conférence : ISPRS 2008, 21st ISPRS world congress 03/07/2008 11/07/2008 Pékin Chine OA ISPRS Archives Importance : pp 85 - 91 Format : 21 x 30 cm Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] impulsion laser
[Termes IGN] lidar à retour d'onde complète
[Termes IGN] milieu urbain
[Termes IGN] semis de points
[Termes IGN] signal lidar
[Termes IGN] traitement du signalRésumé : (auteur) In contrast to conventional airborne multi-echo laser scanner systems, full-waveform (FW) lidar systems are able to record the entire emitted and backscattered signal of each laser pulse. Instead of clouds of individual 3D points, FW devices provide connected 1D profiles of the 3D scene, which contain more detailed and additional information about the structure of the illuminated surfaces. This paper is focused on the analysis of FW data in urban areas. The problem of modelling FW lidar signals is first tackled. The standard method assumes the waveform to be the superposition of signal contributions of each scattering object in such a laser beam, which are approximated by Gaussian distributions. This model is suitable in many cases, especially in vegetated terrain. However, since it is not tailored to urban waveforms, the generalized Gaussian model is selected instead here. Then, a pattern recognition method for urban area classification is proposed. A supervised method using Support Vector Machines is performed on the FW point cloud based on the parameters extracted from the post-processing step. Results show that it is possible to partition urban areas in building, vegetation, natural ground and artificial ground regions with high accuracy using only lidar waveforms. Numéro de notice : C2008-022 Affiliation des auteurs : MATIS+Ext (1993-2011) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : sans En ligne : https://www.isprs.org/proceedings/XXXVII/congress/3_pdf/13.pdf Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=64223 Documents numériques
en open access
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