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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)
[article]
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] GéovisualisationRésumé : (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
in Transactions in GIS > vol 26 n° 4 (June 2022) . - pp 1718 - 1734[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)
[article]
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 urbaineRésumé : (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
in Survey review > vol 52 n° 371 (March 2020) . - pp 150 - 158[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)
[article]
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 cartographiqueRésumé : (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
in Cartographic journal (the) > Vol 55 n° 3 (August 2018) . - pp 273 - 284[article]Réservation
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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)
[article]
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 forestierRésumé : (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
in International journal of applied Earth observation and geoinformation > vol 57 (May 2017) . - pp 36 - 48[article]
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 artificielRésumé : (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)PermalinkSPAWNN: A toolkit for SPatial Analysis With Self-Organizing Neural Networks / Julian Hagenauer in Transactions in GIS, vol 20 n° 5 (October 2016)PermalinkExploration 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)PermalinkCarte 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)PermalinkModel generalization of two different drainage patterns by self-organizing maps / Alper Sen in Cartography and Geographic Information Science, vol 41 n° 2 (March 2014)PermalinkCombining Geo-SOM and hierarchical clustering to explore geospatial data / Chen-Chieh Feng in Transactions in GIS, vol 18 n° 1 (February 2014)PermalinkUse of artificial neural networks for selective omission in updating road networks / Qi Zhou in Cartographic journal (the), vol 51 n° 1 (February 2014)PermalinkPermalinkCarte de Kohonen et classification ascendante hiérarchique pour l’analyse de données géohistoriques / Ana-Maria Olteanu-Raimond (2014)PermalinkThe 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)PermalinkTowards qualitative geovisual analytics: A case study involving places, people, and mediated experience / Ryan Burns in Cartographica, vol 48 n° 3 (October 2013)PermalinkVisual discovery of synchronisation in weather data at multiple temporal resolutions / Xiaojing Wu in Cartographic journal (the), vol 50 n° 3 (August 2013)PermalinkDevelopment of a modified neural network-based land cover classification system using automated data selector and multiresolution remotely sensed data / S. Khorram in Geocarto international, vol 26 n° 6 (October 2011)PermalinkEffect of SRTM resolution on morphometric feature identification using neural network - self organizing map / A. Ehsani in Geoinformatica, vol 14 n° 4 (October 2010)PermalinkUncertainty analysis for the classification of multispectral satellite images using SVMs and SOMs / F. Giacco in IEEE Transactions on geoscience and remote sensing, vol 48 n° 10 (October 2010)PermalinkAutomatic cluster identification for environnemental applications using the self-organizing maps and a new genetic algorithm / T. Oyana in Geocarto international, vol 25 n° 1 (February 2010)PermalinkCarto-Som: Cartogram creation using self-organizing maps / R. Henriques in International journal of geographical information science IJGIS, vol 23 n°3-4 (march - april 2009)PermalinkEarthquake-induced landslide hazard monitoring and assessment using SOM and PROMETHEE techniques: a case study at the Chiufenershan area in Central Taiwan / W.T. Lin in International journal of geographical information science IJGIS, vol 22 n° 8-9 (august 2008)PermalinkLand-cover classification using ASTER: multi-band combinations based on wavelet fusion and SOM neural network / H. Bagan in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 3 (March 2008)PermalinkVisual analysis of network traffic – interactive monitoring, detection, and interpretation of security threats / Florian Mansmann (ca 2008)PermalinkA time-efficient method for anomaly detection in hyperspectral images / O. Duran in IEEE Transactions on geoscience and remote sensing, vol 45 n° 12 Tome 1 (December 2007)PermalinkDetecting man-made structures and changes in satellite imagery with a content-based information retrieval system built on Self-Organizing Maps / Matthieu Molinier in IEEE Transactions on geoscience and remote sensing, vol 45 n° 4 (April 2007)PermalinkMesh simplification for building typification / Dirk Burghardt in International journal of geographical information science IJGIS, vol 21 n° 3-4 (march - april 2007)PermalinkSubpixel analysis of Landsat ETM/sup +/ using self-organizing map (SOM) neural networks for urban land cover characterization / S. Lee in IEEE Transactions on geoscience and remote sensing, vol 44 n° 6 (June 2006)PermalinkEvaluating the usability of visualization methods in an exploratory geovisualization environment / E.L. Koua in International journal of geographical information science IJGIS, vol 20 n° 4 (april 2006)PermalinkReconstructing spatiotemporal trajectories from sparse data / P. Partsinevelos in ISPRS Journal of photogrammetry and remote sensing, vol 60 n° 1 (December 2005 - March 2006)PermalinkAmalgamation in cartographic generalization using Kohonen's feature nets / M.K. Allouche in International journal of geographical information science IJGIS, vol 19 n° 8 - 9 (september 2005)PermalinkOptimization approaches for generalization and data abstraction / Monika Sester in International journal of geographical information science IJGIS, vol 19 n° 8 - 9 (september 2005)PermalinkA statistical self-organizing learning system for remote sensing classification / H.M. Chi in IEEE Transactions on geoscience and remote sensing, vol 43 n° 8 (August 2005)PermalinkAssessment of simulated cognitive maps: the influence of prior knowledge from cartographic maps / R.E. Lloyd in Cartography and Geographic Information Science, vol 32 n° 3 (July 2005)PermalinkVisualizing demographic trajectories with self-organizing maps / A. Skupin in Geoinformatica, vol 9 n° 2 (June - August 2005)PermalinkDesigning fuzzy rule based classifier using self-organizing feature map for analysis of multispectral satellite images / Nikhil R. Pal in International Journal of Remote Sensing IJRS, vol 26 n° 10 (May 2005)PermalinkMultivariate analysis and geovisualization with an integrated geographic knowledge discovery approach / D. Guo in Cartography and Geographic Information Science, vol 32 n° 2 (April 2005)PermalinkEstimation and monitoring of bare soil/vegetation ratio with SPOT vegetation and HRVIR / Grégoire Mercier in IEEE Transactions on geoscience and remote sensing, vol 43 n° 2 (February 2005)PermalinkUsing maximum likelihood (ML) and maximum a prior probability (MAP) in iterative self-organizing data (Isodata) / Hassan A. Karimi in Geocarto international, vol 19 n° 1 (March - May 2004)PermalinkAgile 2004, 7th Agile Conference on Geographic Information Science, Heraklion (Greece), 29 April - 1 May 2004 / Fred Toppen (2004)Permalink