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Unsupervised extraction of urban features from airborne lidar data by using self-organizing maps / Alper Sen in Survey review, vol 52 n° 371 (March 2020)  

Public [article]  inSurvey review > vol 52 n° 371 (March 2020) . - pp 150 - 158 Titre : Unsupervised extraction of urban features from airborne lidar data by using self-organizing maps Type de document : Article/Communication Auteurs : Alper Sen, Auteur ; Baris Suleymanoglu, Auteur ; Metin Soycan, Auteur Année de publication : 2020 Article en page(s) : pp 150 - 158 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] algorithme de filtrage [Termes IGN] carte de Kohonen [Termes IGN] classification non dirigée [Termes IGN] classification par réseau neuronal [Termes IGN] données lidar [Termes IGN] extraction de la végétation [Termes IGN] extraction de points [Termes IGN] filtre adaptatif [Termes IGN] khi carré [Termes IGN] pondération [Termes IGN] réseau neuronal artificiel [Termes IGN] semis de points [Termes IGN] zone urbaine Résumé : (auteur) The extraction of artificial and natural features using light detection and ranging (Lidar) data is a fundamental task in many fields of research for environmental science. In this study, the possibility of using self-organising maps (SOM), which is an unsupervised artificial neural network classification method to extract the bare earth surface and features from airborne Lidar data, was investigated for two different urban areas. The effect of the enlargement of the study area was analysed using the proposed approach. The appropriate weights of SOM inputs, which are 3D coordinates and intensity, obtained from a Lidar point cloud were determined by using Pearson's chi-squared independence test. The weighted SOM feature extraction performance was better than that of the unweighted SOM. The filtering results of SOM to separate ground and non-ground data were also compared with those obtained by the adaptive TIN filtering algorithm. Most of the non-ground features could be removed by the weighted SOM. Numéro de notice : A2020-079 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2018.1532704 Date de publication en ligne : 12/10/2018 En ligne : https://doi.org/10.1080/00396265.2018.1532704 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94642 [article]

An experimental approach for selection/elimination in stream network generalization using support vector machines / Alper Sen in Geocarto international, vol 30 n° 3 - 4 (March - April 2015)  

Public [article]  inGeocarto international > vol 30 n° 3 - 4 (March - April 2015) . - pp 311 - 329 Titre : An experimental approach for selection/elimination in stream network generalization using support vector machines Type de document : Article/Communication Auteurs : Alper Sen, Auteur ; Turkay Gokgoz, Auteur Année de publication : 2015 Article en page(s) : pp 311 - 329 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage automatique [Termes IGN] base de données multi-représentation [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] généralisation cartographique automatisée [Vedettes matières IGN] Généralisation Résumé : (Auteur) Multi-representation databases (MRDB) are used in several Geographical Information System applications for different purposes. MRDB are mainly obtained through model and cartographic generalizations. The model generalization is essentially achieved with the selection/elimination process in which a decision must be made to include or exclude the object at the target level. In this study, support vector machines (SVM) was, for the first time, used for the selection/elimination process in stream network generalization. Within this context, the attributes to be used as input data in the SVM method were determined and weighted according to the associations determined in a chi-squared independence test. 1:100,000-scale (medium resolution) stream networks were derived from two 1:24,000-scale (high resolution) stream networks with different patterns in the United States Geological Survey National Hydrography Data-sets. The derived stream networks were quite similar to the 1:100,000-scale original stream networks in both qualitative and visual aspects. Numéro de notice : A2015-249 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2014.937466 Date de publication en ligne : 25/07/2014 En ligne : https://doi.org/10.1080/10106049.2014.937466 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76247 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2015021	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Model generalization of two different drainage patterns by self-organizing maps / Alper Sen in Cartography and Geographic Information Science, vol 41 n° 2 (March 2014)  

Public [article]  inCartography and Geographic Information Science > vol 41 n° 2 (March 2014) . - pp 151 - 165 Titre : Model generalization of two different drainage patterns by self-organizing maps Type de document : Article/Communication Auteurs : Alper Sen, Auteur ; Turkay Gokgoz, Auteur ; Monika Sester, Auteur Année de publication : 2014 Article en page(s) : pp 151 - 165 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] carte de Kohonen [Termes IGN] Etats-Unis [Termes IGN] généralisation de base de données [Termes IGN] réseau hydrographique [Termes IGN] réseau neuronal artificiel [Vedettes matières IGN] Généralisation Résumé : (Auteur) In this study, we develop a new method using self-organizing maps (SOMs) for the selection of hydrographic model generalization. The most suitable attributes of the stream objects are used as input variables to the SOM. The attributes were weighted using Pearson’s chi-square independence test. We used the Radical Law to determine how many features should be selected, and an incremental approach was developed to determine which clusters should be selected from the SOM. Two drainage patterns (dendritic and modified basic) were obtained from the National Hydrography Datasets of United States Geological Survey at 1:24,000-scale (high resolution) and used in order to derive stream networks at 1:100,000-scale (medium resolution). The 1:100,000-scale stream networks, derived in accordance with the proposed approach, are similar to those in the original maps in both quantity and visual aspects. Stream density and pattern were maintained in each subunit, and continuous and semantically correct networks were obtained. Numéro de notice : A2014-208 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1080/15230406.2013.877231 En ligne : https://doi.org/10.1080/15230406.2013.877231 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33111 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
032-2014021	RAB	Revue	Centre de documentation	En réserve L003	Disponible