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Effect of SRTM resolution on morphometric feature identification using neural network - self organizing map / A. Ehsani in Geoinformatica, vol 14 n° 4 (October 2010)
[article]
Titre : Effect of SRTM resolution on morphometric feature identification using neural network - self organizing map Type de document : Article/Communication Auteurs : A. Ehsani, Auteur ; F. Quiel, Auteur ; A. Malekian, Auteur Année de publication : 2010 Article en page(s) : pp 405 - 424 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] aire protégée
[Termes IGN] Carpates
[Termes IGN] carte de Kohonen
[Termes IGN] classification par réseau neuronal
[Termes IGN] détection de changement
[Termes IGN] données topographiques
[Termes IGN] géomorphométrie
[Termes IGN] image SIR-C-X-SAR
[Termes IGN] MNS SRTMRésumé : (Auteur) In this study, we present a semi-automatic procedure using Neural Networks—Self Organizing Map—and Shuttle Radar Topography Mission DEMs to characterize morphometric features of the landscape in the Man and Biosphere Reserve “Eastern Carpathians”. We investigate specially the effect of two resolutions, SIR-C with 3 arc seconds and X-SAR with 1 arc second for morphometric feature identification. Specifically we investigate how the SRTM/C band data with 30 m interpolated grid, corresponding to SRTM/X band 30 m, affect the morphometric characterization and topography derivatives. To reduce misregistration between the DEMs, spatial co-registration was performed and a RMSE of 0.48 pixel was achieved. Morphometric parameters such as slope, maximum curvature, minimum curvature and cross-sectional curvature are derived using a bivariate quadratic approximation on 90 m, 30 m and interpolated 30 m DEMs. Self Organizing Map (SOM) is used for the classification of morphometric parameters into ten exclusive and exhaustive classes. These classes were analyzed as morphometric features such as ridge, channel, crest line and planar for all data sets based on feature space (scatter plot), morphometric signatures and 3D inspection of the area. The map quality is analyzed by oblique views with contour lines overlaid. Using the X band DEM with 30 m grid as benchmark, a change detection technique was used to quantify differences in morphometric features and to assess the scale effect going from a 90 m (C-band) DEM to an interpolated 30 m DEM. The same procedure is used to study the effect of different resolutions on morphometric features. Morphometric parameters were computed by a moving window size 5 x 5 (corresponding to 450 m on the ground) over SRTM- 90 m. To cover the same ground area, a moving window size of 15 x 15 is used for the 30 m DEM. The change analysis showed the amount of resolution dependency of morphometric features. Overall, the results showed that the introduced method is very useful for identification of morphometric features based on SRTM resolution. Decreasing the grid size from 90 m to 30 m reveals considerably more detailed information emphasizing local conditions. Comparison between results from DEM-30 m as reference data set and interpolated 30 m, showed a rate of change of 31.5% which is negligible. About 17% of this rate correspond to classes with mean slope > 10°. Of the morphometric parameters, the cross sectional curvature is most sensitive to DEM resolution. Increasing spatial resolution reduces the main constrains for morphometric analysis with SRTM 90 m data, such as unrealistic features and isolated single elements in the output map. So in case of lack of high resolution data, the SRTM 90 m data could be interpolated and used for further geomorphic analysis. Copyright Springer Numéro de notice : A2010-302 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s10707-009-0085-4 Date de publication en ligne : 29/04/2009 En ligne : https://doi.org/10.1007/s10707-009-0085-4 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30496
in Geoinformatica > vol 14 n° 4 (October 2010) . - pp 405 - 424[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 057-2010041 RAB Revue Centre de documentation En réserve L003 Disponible vol 24 n° 10 - october 2010 - Geospatial visual analytics : focus on time. Special issue of the ICA commission on geovisualization (Bulletin de International journal of geographical information science IJGIS) / Gennady Adrienko
[n° ou bulletin]
Titre : vol 24 n° 10 - october 2010 - Geospatial visual analytics : focus on time. Special issue of the ICA commission on geovisualization Type de document : Périodique Auteurs : Gennady Adrienko, Éditeur scientifique ; Natalia Adrienko, Éditeur scientifique ; Jason Dykes, Éditeur scientifique ; Menno-Jan Kraak, Éditeur scientifique ; Heidrun Schumann, Éditeur scientifique Année de publication : 2010 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] analyse spatio-temporelle
[Termes IGN] analyse visuelle
[Termes IGN] exploration de données géographiquesNuméro de notice : 079-201006 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Numéro de périodique Permalink : https://documentation.ensg.eu/index.php?lvl=bulletin_display&id=13726 [n° ou bulletin]Contient
- Assessing the quality of geoscientific simulation models with visual analytics methods: a design study / D. Dransch in International journal of geographical information science IJGIS, vol 24 n° 10 (october 2010)
- Using space-time visual analytic methods for exploring the dynamics of ethnic groups' residential patterns / I. Omer in International journal of geographical information science IJGIS, vol 24 n° 10 (october 2010)
- Visualization of attributed hierarchical structures in a spatiotemporal context / S. Hadlak in International journal of geographical information science IJGIS, vol 24 n° 10 (october 2010)
- Analysing spatio-temporal autocorrelation with LISTA-Viz / F. Hardisty in International journal of geographical information science IJGIS, vol 24 n° 10 (october 2010)
- An integrated approach for visual analysis of a multisource moving objects knowledge base / N. Wllems in International journal of geographical information science IJGIS, vol 24 n° 10 (october 2010)
- Space-time density of trajectories : exploring spatio-temporal patterns in movement data / Urška Demšar in International journal of geographical information science IJGIS, vol 24 n° 10 (october 2010)
- Exploring the efficiency of users' visual analytics strategies based on sequence analysis of eye movement recordings / Arzu Çöltekin in International journal of geographical information science IJGIS, vol 24 n° 10 (october 2010)
- Space, time and visual analytics / Gennady Adrienko in International journal of geographical information science IJGIS, vol 24 n° 10 (october 2010)
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Code-barres Cote Support Localisation Section Disponibilité 079-2010061 RAB Revue Centre de documentation En réserve L003 Disponible 079-2010062 RAB Revue Centre de documentation En réserve L003 Disponible Similarity weighted instance-based learning for the generation of transition potentials in land use change modeling / F. Sangermano in Transactions in GIS, vol 14 n° 5 (October 2010)
[article]
Titre : Similarity weighted instance-based learning for the generation of transition potentials in land use change modeling Type de document : Article/Communication Auteurs : F. Sangermano, Auteur ; J. Ronald Eastman, Auteur ; H. Zhu, Auteur Année de publication : 2010 Article en page(s) : pp 569 - 580 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] algorithme d'apprentissage
[Termes IGN] analyse comparative
[Termes IGN] apprentissage automatique
[Termes IGN] classification barycentrique
[Termes IGN] déboisement
[Termes IGN] modélisation spatio-temporelle
[Termes IGN] Perceptron multicouche
[Termes IGN] similitude
[Termes IGN] traitement de données localisées
[Termes IGN] utilisation du solRésumé : (Auteur) Land use change models are increasingly being used to evaluate the effect of land change on climate and biodiversity and to generate scenarios of deforestation. Although many methods are available to model land transition potentials, they are usually not user-friendly and require the specification of many parameters, making the task difficult for decision makers not familiar with the tools, as well as making the process difficult to interpret. In this article we propose a simple method for modeling transition potentials. SimWeight is an instance-based learning algorithm based on the logic of the K-Nearest Neighbor algorithm. The method identifies the relevance of each driver variable and predicts the transition potential of locations given known instances of change. A case study was used to demonstrate and validate the method. Comparison of results with the Multi-Layer Perceptron neural network (MLP) suggests that SimWeight performs similarly in its capacity to predict transition potentials, without the need for complex parameters. Another advantage of SimWeight is that it is amenable to parallelization for deployment on a cloud computing platform. Numéro de notice : A2010-496 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/j.1467-9671.2010.01226.x Date de publication en ligne : 23/11/2010 En ligne : https://doi.org/10.1111/j.1467-9671.2010.01226.x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30689
in Transactions in GIS > vol 14 n° 5 (October 2010) . - pp 569 - 580[article]Space-time density of trajectories : exploring spatio-temporal patterns in movement data / Urška Demšar in International journal of geographical information science IJGIS, vol 24 n° 10 (october 2010)
[article]
Titre : Space-time density of trajectories : exploring spatio-temporal patterns in movement data Type de document : Article/Communication Auteurs : Urška Demšar, Auteur ; K. Virrantaus, Auteur Année de publication : 2010 Article en page(s) : pp 1527 - 1542 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse spatio-temporelle
[Termes IGN] densité
[Termes IGN] données localisées 3D
[Termes IGN] données spatiotemporelles
[Termes IGN] estimation par noyau
[Termes IGN] exploration de données géographiques
[Termes IGN] Finlande
[Termes IGN] navire
[Termes IGN] reconstruction d'itinéraire ou de trajectoire
[Termes IGN] trajectoire (véhicule non spatial)Résumé : (Auteur) Modern positioning and identification technologies enable tracking of almost any type of moving object. A remarkable amount of new trajectory data is thus available for the analysis of various phenomena. In cartography, a typical way to visualise and explore such data is to use a space-time cube, where trajectories are shown as 3D polylines through space and time. With increasingly large movement datasets becoming available, this type of display quickly becomes cluttered and unclear. In this article, we introduce the concept of 3D space-time density of trajectories to solve the problem of cluttering in the space-time cube. The space-time density is a generalisation of standard 2D kernel density around 2D point data into 3D density around 3D polyline data (i.e. trajectories). We present the algorithm for space-time density, test it on simulated data, show some basic visualisations of the resulting density volume and observe particular types of spatio-temporal patterns in the density that are specific to trajectory data. We also present an application to real-time movement data, that is, vessel movement trajectories acquired using the Automatic Identification System (AIS) equipment on ships in the Gulf of Finland. Finally, we consider the wider ramifications to spatial analysis of using this novel type of spatio-temporal visualisation. Numéro de notice : A2010-466 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658816.2010.511223 En ligne : https://doi.org/10.1080/13658816.2010.511223 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30659
in International journal of geographical information science IJGIS > vol 24 n° 10 (october 2010) . - pp 1527 - 1542[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2010061 RAB Revue Centre de documentation En réserve L003 Disponible 079-2010062 RAB Revue Centre de documentation En réserve L003 Disponible Uncertainty 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)
[article]
Titre : Uncertainty analysis for the classification of multispectral satellite images using SVMs and SOMs Type de document : Article/Communication Auteurs : F. Giacco, Auteur ; C. Thiel, Auteur ; L. Pugliese, Auteur ; S. Scarpetta, Auteur ; M. Marinaro, Auteur Année de publication : 2010 Article en page(s) : pp 3769 - 3779 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] carte d'occupation du sol
[Termes IGN] carte de Kohonen
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image multibande
[Termes IGN] incertitude des donnéesRésumé : (Auteur) Classification of multispectral remotely sensed data with textural features is investigated with a special focus on uncertainty analysis in the produced land-cover maps. Much effort has already been directed into the research of satisfactory accuracy-assessment techniques in image classification, but a common approach is not yet universally adopted. We look at the relationship between hard accuracy and the uncertainty on the produced answers, introducing two measures based on maximum probability and quadratic entropy. Their impact differs depending on the type of classifier. In this paper, we deal with two different classification strategies, based on support vector machines (SVMs) and Kohonen's self-organizing maps (SOMs), both suitably modified to give soft answers. Once the multiclass probability answer vector is available for each pixel in the image, we studied the behavior of the overall classification accuracy as a function of the uncertainty associated with each vector, given a hard-labeled test set. The experimental results show that the SVM with one-versus-one architecture and linear kernel clearly outperforms the other supervised approaches in terms of overall accuracy. On the other hand, our analysis reveals that the proposed SOM-based classifier, despite its unsupervised learning procedure, is able to provide soft answers which are the best candidates for a fusion with supervised results. Numéro de notice : A2010-475 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2010.2047863 Date de publication en ligne : 27/05/2010 En ligne : https://doi.org/10.1109/TGRS.2010.2047863 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30668
in IEEE Transactions on geoscience and remote sensing > vol 48 n° 10 (October 2010) . - pp 3769 - 3779[article]Réservation
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