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A multi-resolution hybrid approach for building model reconstruction from lidar data / M. Satari in Photogrammetric record, vol 27 n° 139 (September - November 2012)
[article]
Titre : A multi-resolution hybrid approach for building model reconstruction from lidar data Type de document : Article/Communication Auteurs : M. Satari, Auteur ; F. Samadzadegan, Auteur ; A. Azizi, Auteur ; Hans-Gerd Maas, Auteur Année de publication : 2012 Article en page(s) : pp 330 - 359 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] architecture orientée modèle
[Termes IGN] classification floue
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] photogrammétrie terrestre
[Termes IGN] reconstruction 3D du bâti
[Termes IGN] semis de points
[Termes IGN] séparateur à vaste marge
[Termes IGN] toit
[Termes IGN] transformation de HoughRésumé : (Auteur) In this paper, a multi-resolution hybrid approach is proposed for the reconstruction of building models from point clouds of lidar data. The detection of the main roof planes is obtained through a polyhedral approach, whereas the models of appended parts, in this case the dormers, are reconstructed by adopting a model-driven approach. Clustering of the roof points in a multi-resolution space is based on the fuzzy c-mean in the polyhedral section of this hybrid approach. A weighted plane algorithm is developed in order to determine the planes of each cluster. The verification of planes between multi-resolution spaces adopts a method based on a least squares support vector machine that, in the model-driven section, is applied for detecting types of projecting structures. A method is then developed to determine the dormer models’ parameters. Finally, the detection of boundary roof lines is obtained through a customised fuzzy Hough transform. The paper outlines the concept of the algorithms and the processing chain, and illustrates the results obtained by applying the technique to buildings of different complexities. Numéro de notice : A2012-465 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/j.1477-9730.2012.00688.x Date de publication en ligne : 18/09/2012 En ligne : https://doi.org/10.1111/j.1477-9730.2012.00688.x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31911
in Photogrammetric record > vol 27 n° 139 (September - November 2012) . - pp 330 - 359[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 106-2012031 RAB Revue Centre de documentation En réserve L003 Disponible Parameterizing support vector machines for land cover classification / X. Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 77 n° 1 (January 2011)
[article]
Titre : Parameterizing support vector machines for land cover classification Type de document : Article/Communication Auteurs : X. Yang, Auteur Année de publication : 2011 Article en page(s) : pp 27 - 37 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image Landsat-TM
[Termes IGN] occupation du sol
[Termes IGN] séparateur à vaste margeRésumé : (Auteur) The support vector machine is a group of relatively novel statistical learning algorithms that have not been extensively exploited in the remote sensing community. In previous studies they have been found to generally outperform some popular classifiers. Several recent studies found that training samples and input data dimensionalities can affect image classification accuracies by those popular classifiers and support vector machines alike. The current study extends beyond these recent research frameworks and into another important inquiry area addressing the impacts of internal parameterization on the performance of support vector machines for land-cover classification. A set of support vector machines with different combinations of kernel types, parameters, and error penalty are carefully constructed to classify a Landsat Thematic Mapper image into eight major land-cover categories using identical training data. The accuracy of each classified map is further evaluated using identical reference data. The results reveal that kernel types and error penalty can substantially affect the classification accuracy, and that a careful selection of parameter settings can help improve the performance of the support vector classification. These findings reported here can help establish a practical guidance on the use of support vector machines for land-cover classification from remote sensor data. Numéro de notice : A2011-002 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.77.1.27 En ligne : https://doi.org/10.14358/PERS.77.1.27 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30784
in Photogrammetric Engineering & Remote Sensing, PERS > vol 77 n° 1 (January 2011) . - pp 27 - 37[article]Optimizing Support Vector Machine learning for semi-arid vegetation mapping by using clustering analysis / L. Su in ISPRS Journal of photogrammetry and remote sensing, vol 64 n° 4 (July - August 2009)
[article]
Titre : Optimizing Support Vector Machine learning for semi-arid vegetation mapping by using clustering analysis Type de document : Article/Communication Auteurs : L. Su, Auteur Année de publication : 2009 Article en page(s) : pp 407 - 413 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse de groupement
[Termes IGN] apprentissage automatique
[Termes IGN] carte de la végétation
[Termes IGN] classification ascendante hiérarchique
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] occupation du sol
[Termes IGN] précision de la classification
[Termes IGN] séparateur à vaste marge
[Termes IGN] zone semi-arideRésumé : (Auteur) In remote sensing communities, support vector machine (SVM) learning has recently received increasing attention. SVM learning usually requires large memory and enormous amounts of computation time on large training sets. According to SVM algorithms, the SVM classification decision function is fully determined by support vectors, which compose a subset of the training sets. In this regard, a solution to optimize SVM learning is to efficiently reduce training sets. In this paper, a data reduction method based on agglomerative hierarchical clustering is proposed to obtain smaller training sets for SVM learning. Using a multiple angle remote sensing dataset of a semi-arid region, the effectiveness of the proposed method is evaluated by classification experiments with a series of reduced training sets. The experiments show that there is no loss of SVM accuracy when the original training set is reduced to 34% using the proposed approach. Maximum likelihood classification (MLC) also is applied on the reduced training sets. The results show that MLC can also maintain the classification accuracy. This implies that the most informative data instances can be retained by this approach. Copyright ISPRS Numéro de notice : A2009-297 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2009.02.002 En ligne : https://doi.org/10.1016/j.isprsjprs.2009.02.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29927
in ISPRS Journal of photogrammetry and remote sensing > vol 64 n° 4 (July - August 2009) . - pp 407 - 413[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-09041 SL Revue Centre de documentation Revues en salle Disponible vol 29 n° 21 - October 2008 - Satellite observations of the atmosphere, oceans and their interface in relation to climate, natural hazards and management of coastal zone (Bulletin de International Journal of Remote Sensing IJRS) / G. Levy
[n° ou bulletin]
est un bulletin de International Journal of Remote Sensing IJRS / Remote sensing and photogrammetry society (1980 -)
Titre : vol 29 n° 21 - October 2008 - Satellite observations of the atmosphere, oceans and their interface in relation to climate, natural hazards and management of coastal zone Type de document : Périodique Auteurs : G. Levy, Éditeur scientifique ; J. Gower, Éditeur scientifique ; Remote sensing and photogrammetry society, Auteur Année de publication : 2008 Importance : 404 p. Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Télédétection
[Termes IGN] aérosol
[Termes IGN] littoral
[Termes IGN] océanographie
[Termes IGN] satellite d'observation de la mer
[Termes IGN] séparateur à vaste marge
[Termes IGN] température de surface de la merNuméro de notice : 080-0813 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Numéro de périodique Permalink : https://documentation.ensg.eu/index.php?lvl=bulletin_display&id=9930 [n° ou bulletin]Contient
- Multisensor satellite monitoring of seawater state and oil pollution in the northeastern coastal zone of the Black Sea / S. Shcherbak in International Journal of Remote Sensing IJRS, vol 29 n° 21 (October 2008)
- Extreme wind conditions observed by satellite synthetic aperture radar in the North West Pacific / A. Reppucci in International Journal of Remote Sensing IJRS, vol 29 n° 21 (October 2008)
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Code-barres Cote Support Localisation Section Disponibilité 080-08131 RAB Revue Centre de documentation En réserve L003 Exclu du prêt vol 74 n° 10 - October 2008 - Artificial intelligence in remote sensing (Bulletin de Photogrammetric Engineering & Remote Sensing, PERS) / American society for photogrammetry and remote sensing
[n° ou bulletin]
est un bulletin de Photogrammetric Engineering & Remote Sensing, PERS / American society for photogrammetry and remote sensing (1975 -)
Titre : vol 74 n° 10 - October 2008 - Artificial intelligence in remote sensing Type de document : Périodique Auteurs : American society for photogrammetry and remote sensing, Auteur Année de publication : 2008 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Télédétection
[Termes IGN] algorithme génétique
[Termes IGN] automate cellulaire
[Termes IGN] intelligence artificielle
[Termes IGN] réseau neuronal artificiel
[Termes IGN] séparateur à vaste margeNuméro de notice : 105-0810 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Numéro de périodique Permalink : https://documentation.ensg.eu/index.php?lvl=bulletin_display&id=10346 [n° ou bulletin]Contient
- Subpixel urban land cover estimation: comparing cubist, random forests, and support vector regression / J. Walton in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 10 (October 2008)
- Neuro-fuzzy based analysis of hyperspectral imagery / F. Qiu in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 10 (October 2008)
- Genetic algorithms for the calibration of cellular automata urban growth modeling / J. Shan in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 10 (October 2008)
Subpixel urban land cover estimation: comparing cubist, random forests, and support vector regression / J. Walton in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 10 (October 2008)PermalinkCAp 2006, 8e conférence francophone sur l'apprentissage automatique, 22 - 24 mai 2006, Trégastel, France / Laurent Miclet (2006)Permalink7e conférence francophone sur l'apprentissage automatique, CAp 2005, [Plate-forme AFIA], 30 mai - 3 juin 2005, Nice, France / François Denis (2005)Permalink7es Rencontres des Jeunes Chercheurs en Intelligence Artificielle [Plate-forme AFIA 2005] / Emmanuel Guéré (2005)PermalinkApprentissage automatique / Marc Sebban (1999)PermalinkConférence d'apprentissage 99, actes de CAP'99, Ecole Polytechnique, Palaiseau, 15-18 juin 1999 / Michèle Sebag (1999)Permalink