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Self-organizing maps as a dimension reduction approach for spatial global sensitivity analysis visualization / Seda Şalap-Ayça in Transactions in GIS, vol 26 n° 4 (June 2022)  

Public [article]  inTransactions in GIS > vol 26 n° 4 (June 2022) . - pp 1718 - 1734 Titre : Self-organizing maps as a dimension reduction approach for spatial global sensitivity analysis visualization Type de document : Article/Communication Auteurs : Seda Şalap-Ayça, Auteur Année de publication : 2022 Article en page(s) : pp 1718 - 1734 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse de groupement [Termes IGN] analyse de sensibilité [Termes IGN] carte de Kohonen [Termes IGN] représentation spatiale [Termes IGN] réseau neuronal artificiel [Termes IGN] visualisation cartographique [Termes IGN] voisinage (relation topologique) [Vedettes matières IGN] Géovisualisation Résumé : (auteur) Spatial global sensitivity analysis (SGSA) reveals and ranks the input–output relation in spatial models. The SGSA output is twofold: (1) first-order effects which are the linear relations of every input layer with the output; and (2) high-order effects where the nonlinear interaction among input layers is depicted. The resulting sensitivity maps are twice the number of input layers which is challenging to visualize, considering the limitations of the human cognitive system or visual representations. Finding similar patterns and projecting that similarity into a 2D surface will help to tackle this voluminous visual load. This article presents the implementation of self-organizing maps (SOM), a type of artificial neural network, as a dimension reduction approach for SGSA visualization. SOM is also used for feature selection to identify the most relevant feature for model uncertainty. The winning neurons at SOM are projected as the influence map and the results are compared with conventional visualization techniques. Numéro de notice : A2022-532 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1111/tgis.12963 Date de publication en ligne : 21/06/2022 En ligne : https://doi.org/10.1111/tgis.12963 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101080 [article]

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]

Improving the quality of cartographic colour reproduction using the self-organizing map method / Mingguang Wu in Cartographic journal (the), Vol 55 n° 3 (August 2018)  

Public [article]  inCartographic journal (the) > Vol 55 n° 3 (August 2018) . - pp 273 - 284 Titre : Improving the quality of cartographic colour reproduction using the self-organizing map method Type de document : Article/Communication Auteurs : Mingguang Wu, Auteur ; A - Xing Zhu, Auteur ; Li He, Auteur Année de publication : 2018 Article en page(s) : pp 273 - 284 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes IGN] carte de Kohonen [Termes IGN] carte thématique [Termes IGN] couleur (rédaction cartographique) [Termes IGN] couleur imprimée [Termes IGN] qualité cartographique [Termes IGN] représentation cartographique Résumé : (Auteur) Colour distortion, which is caused by the unavoidable mismatch between a map’s gamut and a device’s gamut, negatively affects the semiotic quality of maps. Cartographic communication often suffers from undesirable colour inconsistency. This method models cartographic colour reproduction as a constrained transform problem, namely, adapting a map’s gamut to fit a device’s gamut while preserving the semiotic quality. First, the characteristics of the map’s gamut are investigated by considering cartographic principles, and the fundamental concerns of preserving semiotic quality are proposed. Then, the self-organizing map method is introduced to iteratively optimize the cartographic colour reproduction. We implement this method and evaluate it based on a series of thematic maps. The results indicate that the proposed algorithm offers better results than two alternatives in terms of facilitating cartographic colour reproduction. Numéro de notice : A2018-519 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00087041.2017.1414106 Date de publication en ligne : 18/10/2018 En ligne : https://doi.org/10.1080/00087041.2017.1414106 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91325 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
030-2018031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

An unsupervised two-stage clustering approach for forest structure classification based on X-band InSAR data — A case study in complex temperate forest stands / Sahra Abdullahi in International journal of applied Earth observation and geoinformation, vol 57 (May 2017)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 57 (May 2017) . - pp 36 - 48 Titre : An unsupervised two-stage clustering approach for forest structure classification based on X-band InSAR data — A case study in complex temperate forest stands Type de document : Article/Communication Auteurs : Sahra Abdullahi, Auteur ; Mathias Schardt, Auteur ; Hans Pretzsch, Auteur Année de publication : 2017 Article en page(s) : pp 36 - 48 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] bande X [Termes IGN] Bavière (Allemagne) [Termes IGN] carte de Kohonen [Termes IGN] classification barycentrique [Termes IGN] classification non dirigée [Termes IGN] distance euclidienne [Termes IGN] forêt tempérée [Termes IGN] image radar moirée [Termes IGN] image TanDEM-X [Termes IGN] image TerraSAR-X [Termes IGN] interféromètrie par radar à antenne synthétique [Termes IGN] structure d'un peuplement forestier Résumé : (auteur) Forest structure at stand level plays a key role for sustainable forest management, since the biodiversity, productivity, growth and stability of the forest can be positively influenced by managing its structural diversity. In contrast to field-based measurements, remote sensing techniques offer a cost-efficient opportunity to collect area-wide information about forest stand structure with high spatial and temporal resolution. Especially Interferometric Synthetic Aperture Radar (InSAR), which facilitates worldwide acquisition of 3d information independent from weather conditions and illumination, is convenient to capture forest stand structure. This study purposes an unsupervised two-stage clustering approach for forest structure classification based on height information derived from interferometric X-band SAR data which was performed in complex temperate forest stands of Traunstein forest (South Germany). In particular, a four dimensional input data set composed of first-order height statistics was non-linearly projected on a two-dimensional Self-Organizing Map, spatially ordered according to similarity (based on the Euclidean distance) in the first stage and classified using the k-means algorithm in the second stage. The study demonstrated that X-band InSAR data exhibits considerable capabilities for forest structure classification. Moreover, the unsupervised classification approach achieved meaningful and reasonable results by means of comparison to aerial imagery and LiDAR data. Numéro de notice : A2017-368 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2016.12.010 En ligne : https://doi.org/10.1016/j.jag.2016.12.010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85785 [article]

Artificial neural networks in geospatial analysis / Sucharita Gopal (2017)  

Public Titre : Artificial neural networks in geospatial analysis Type de document : Chapitre/Contribution Auteurs : Sucharita Gopal, Auteur Editeur : New York, Londres, Hoboken (New Jersey), ... : John Wiley & Sons Année de publication : 2017 Importance : 7 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] apprentissage dirigé [Termes IGN] apprentissage non-dirigé [Termes IGN] apprentissage semi-dirigé [Termes IGN] carte de Kohonen [Termes IGN] Perceptron multicouche [Termes IGN] réseau neuronal artificiel Résumé : (Auteur) [introduction] Artificial neural networks (ANN) are computational models inspired by and designed to simulate biological nervous systems that are capable of performing specific information-processing tasks such as data classification and pattern recognition. ANN seeks to replicate the massively parallel nature of a biological neural network. A neural network is a system composed of many simple processing nodes whose function is determined by network structure and connection strengths. Numéro de notice : H2017-023 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Chapître / contribution Date de publication en ligne : 23/02/2016 En ligne : https://doi.org/10.1002/9781118786352.wbieg0322 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90378

Joint analysis of passive and active land surface responses for Global Precipitation Measurement / Iris de Gelis (2017) 

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SPAWNN: A toolkit for SPatial Analysis With Self-Organizing Neural Networks / Julian Hagenauer in Transactions in GIS, vol 20 n° 5 (October 2016)  

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Exploration of spatiotemporal and semantic clusters of Twitter data using unsupervised neural networks / Enrico Steiger in International journal of geographical information science IJGIS, vol 30 n° 9-10 (September - October 2016)  

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Carte de Kohonen et classification ascendante hiérarchique pour l’analyse de données géohistoriques / Ana-Maria Olteanu-Raimond in Revue internationale de géomatique, vol 25 n° 4 (octobre - décembre 2015)  

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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)  

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Combining Geo-SOM and hierarchical clustering to explore geospatial data / Chen-Chieh Feng in Transactions in GIS, vol 18 n° 1 (February 2014)  

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Use of artificial neural networks for selective omission in updating road networks / Qi Zhou in Cartographic journal (the), vol 51 n° 1 (February 2014)  

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Automatische Interpretation von Semantik aus digitalen Karten im World Wide Web / Fen Luo (2014)  

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Carte de Kohonen et classification ascendante hiérarchique pour l’analyse de données géohistoriques / Ana-Maria Olteanu-Raimond (2014) 

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The signature of self-organisation in cities: Temporal patterns of clustering and growth in street networks / Kinda Al-Sayed in Revue internationale de géomatique, vol 23 n° 3 - 4 (septembre 2013 - février 2014)  

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