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Termes IGN > mathématiques > statistique mathématique > analyse de données > classification > classification barycentrique
classification barycentriqueSynonyme(s)classification sur la distance minimale ;classification du k-proche voisin ;classification par minimum de distance classification par k centroïdesVoir aussi |
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[article]
Titre : In-route nearest neighbor queries Type de document : Article/Communication Auteurs : J.S. Yoo, Auteur ; Shashi Shekhar, Auteur Année de publication : 2005 Article en page(s) : pp 117 - 137 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] analyse comparative
[Termes IGN] base de données routières
[Termes IGN] classification barycentrique
[Termes IGN] coût
[Termes IGN] plus proche voisin, algorithme du
[Termes IGN] requête spatiale
[Termes IGN] système d'information géographiqueRésumé : (Auteur) Nearest neighbor query is one of the most important operations in spatial databases and their application domains, such as location-based services and advanced traveler information systems. This paper addresses the problem of finding the in-route nearest neighbor (IRNN) for a query object tuple which consists of a given route with a destination and a current location on it. The IRNN is a facility instance via which the detour from the original route on the way to the destination is smallest. This paper addresses four alternative solution methods Comparisons among them are presented using an experimental framework. Extensive experiments using real road map datasets are conducted to examine the behaviors of the solutions in terms of five parameters affecting the performance. The overall experiments show that our strategy to reduce the expensive path-computations to minimize the response time is reasonable. The spatial distance join-based method always shows better performance with fewer path computations compared to the recursive methods. The computation costs for all methods except the precomputed zone-based method increase with increases in the road map size and the query route length but decrease with increases in the facility density. The precomputed zone-based method shows the most efficiency when there are no updates on the road map. Numéro de notice : A2005-223 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1007/s10707-005-6671-1 En ligne : https://doi.org/10.1007/s10707-005-6671-1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27360
in Geoinformatica > vol 9 n° 2 (June - August 2005) . - pp 117 - 137[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 057-05021 RAB Revue Centre de documentation En réserve L003 Disponible Combining spectral and spatial information into hidden Markov models for unsupervised image classification / B. Tso in International Journal of Remote Sensing IJRS, vol 26 n° 10 (May 2005)
[article]
Titre : Combining spectral and spatial information into hidden Markov models for unsupervised image classification Type de document : Article/Communication Auteurs : B. Tso, Auteur ; C. Olsen, Auteur Année de publication : 2005 Article en page(s) : pp 2113 - 2133 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] classification barycentrique
[Termes IGN] classification contextuelle
[Termes IGN] classification non dirigée
[Termes IGN] données localisées 2D
[Termes IGN] image multi sources
[Termes IGN] modèle de Markov
[Termes IGN] optimisation (mathématiques)
[Termes IGN] précision de la classification
[Termes IGN] qualité des donnéesRésumé : (Auteur) Unsupervised classification methodology applied to remote sensing image processing can provide benefits in automatically converting the raw image data into useful information so long as higher classification accuracy is achieved. The traditional k-means clustering scheme using spectral data alone does not perform well in general as far as accuracy is concerned. This is partly due to the failure to take the spatial inter-pixels dependencies (i.e. the context) into account, resulting in a 'busy' visual appearance to the output imagery. To address this, the hidden Markov models (HMM) are introduced in this study as a fundamental framework to incorporate both the spectral and contextual information in analysis. This helps generate more patch-like output imagery and produces higher classification accuracy in an unsupervised scheme. The newly developed unsupervised classification approach is based on observation-sequence and observation-density adjustments, which have been proposed for incorporating 2D spatial information into the linear HMM. For the observation-sequence adjustment methods, there are a total of five neighbourhood systems being proposed. Two neighbourhood systems were incorporated into the observation-density methods for study. The classification accuracy is then evaluated by means of confusion matrices made by randomly chosen test samples. The classification obtained by k-means clustering and the HMM with commonly seen strip-like and Hilbert-Peano sequence fitting methods were also measured. Experimental results showed that the proposed approaches for combining both the spectral and spatial information into HMM unsupervised classification mechanism present improvements in both classification accuracy and visual qualities. Numéro de notice : A2005-259 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160512331337844 En ligne : https://doi.org/10.1080/01431160512331337844 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27395
in International Journal of Remote Sensing IJRS > vol 26 n° 10 (May 2005) . - pp 2113 - 2133[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 080-05101 RAB Revue Centre de documentation En réserve L003 Disponible Land covers update by supervised classification of segmented ASTER images / A.R.S. Marcal in International Journal of Remote Sensing IJRS, vol 26 n° 7 (April 2005)
[article]
Titre : Land covers update by supervised classification of segmented ASTER images Type de document : Article/Communication Auteurs : A.R.S. Marcal, Auteur ; J.S. Borges, Auteur ; et al., Auteur Année de publication : 2005 Article en page(s) : pp 1347 - 1362 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse discriminante
[Termes IGN] carte d'occupation du sol
[Termes IGN] classificateur paramétrique
[Termes IGN] classification barycentrique
[Termes IGN] classification dirigée
[Termes IGN] classification floue
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image multibande
[Termes IGN] image Terra-ASTER
[Termes IGN] mise à jour cartographique
[Termes IGN] Portugal
[Termes IGN] segmentation d'imageRésumé : (Auteur) The revision of the 1995 land cover dataset for the Vale do Sousa region, in the northwest of Portugal, was carried out by supervised classification of a multispectral image from the Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) sensor. The nine reflective bands of ASTER were used, covering the spectral range from 0.52-2.43 um. The image was initially ortho-rectified and segmented into 51 186 objects, with an average object size of 135 pixels (about 3 ha). A total of 582 of these objects were identified for training nine land cover classes. The image was classified using an algorithm based on a fuzzy classifier, Support Vector Machines (SVM), K Nearest Neighbours (K-NN) and a Logistic Discrimination (LD) classifier. The results from the classification were evaluated using a set of 277 validation sites, independently gathered. The overall accuracy was 44.6%, for the fuzzy classifier. 70.5%, for the SVM, 60.9% for the K-NN and 72.2% for the LD classifier. The difficulty in discriminating between some of the forest land cover classes was examined by separability analysis and unsupervised classification with hierarchical clustering. The forest classes were found to overlap in the multi-spectral space defined by the nine ASTER bands used. Numéro de notice : A2005-179 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160412331291233 En ligne : https://doi.org/10.1080/01431160412331291233 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27316
in International Journal of Remote Sensing IJRS > vol 26 n° 7 (April 2005) . - pp 1347 - 1362[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 080-05071 RAB Revue Centre de documentation En réserve L003 Exclu du prêt Use of the Bradley-Terry model to quantify association in remotely sensed images / Alfred Stein in IEEE Transactions on geoscience and remote sensing, vol 43 n° 4 (April 2005)
[article]
Titre : Use of the Bradley-Terry model to quantify association in remotely sensed images Type de document : Article/Communication Auteurs : Alfred Stein, Auteur ; J. Aryal, Auteur ; G. Gort, Auteur Année de publication : 2005 Article en page(s) : pp 852 - 856 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] classification barycentrique
[Termes IGN] classification par la distance de Mahalanobis
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] estimation de précision
[Termes IGN] estimation des paramètres
[Termes IGN] image Ikonos
[Termes IGN] image Terra-ASTER
[Termes IGN] Pays-BasRésumé : (Auteur) Thematic maps prepared from remotely sensed images require a statistical accuracy assessment. For this purpose, the k-statistic is often used. This statistic does not distinguish between whether one unit is classified as another, or vice versa. In this paper, the Bradley-Terry (BT) model is applied for accuracy assessment. This model compares categories pairwise. The probability of one class over another class is estimated as well as the expected values of class pixels. The study is illustrated with an Advanced Spaceborne Thermal Emission and Reflection Radiometer image from the Netherlands, to which a maximum-likelihood classification with the Euclidean distance is applied. An error matrix is generated using an IKONOS image from the same area as ground truth. It is shown to which degree the BT model extends the K-statistic. A comparison with the Mahalanobis distance is made. Standardization is carried out to overcome problems emerging from the fact that a common BT model does not include the number of correctly classified pixels. The study shows how the BT model serves as an alternative to the usual k-statistic. Numéro de notice : A2005-193 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2005.843569 En ligne : https://doi.org/10.1109/TGRS.2005.843569 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27330
in IEEE Transactions on geoscience and remote sensing > vol 43 n° 4 (April 2005) . - pp 852 - 856[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-05042 RAB Revue Centre de documentation En réserve L003 Disponible Spatio-temporal dynamics in California's central valley: empirical links to urban theory / C. Dietzel in International journal of geographical information science IJGIS, vol 19 n° 2 (february 2005)
[article]
Titre : Spatio-temporal dynamics in California's central valley: empirical links to urban theory Type de document : Article/Communication Auteurs : C. Dietzel, Auteur ; Martin Herold, Auteur ; J.J. Hemphill, Auteur ; K.C. Clarke, Auteur Année de publication : 2005 Article en page(s) : pp 175 - 195 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] analyse spatiale
[Termes IGN] Californie (Etats-Unis)
[Termes IGN] classification barycentrique
[Termes IGN] croissance urbaine
[Termes IGN] densité de population
[Termes IGN] échelle géographique
[Termes IGN] modèle conceptuel de données spatio-temporelles
[Termes IGN] population urbaine
[Termes IGN] spatial metrics
[Termes IGN] urbanisation
[Termes IGN] villeRésumé : (Auteur) This paper explores an addition to theory in urban geography pertaining to spatio-temporal dynamics. Remotely sensed data on the historical extent of urban areas were used in a spatial metrics analysis of geographical form of towns and cities in the Central Valley of California (USA). Regularities in the spatiotemporal pattern of urban growth were detected and characterized over a hundred year period. To test hypotheses about variation over geographical scale, multiple spatial extents were used in examining a set of spatial metric values including an index of contagion, the mean nearest neighbor distance, urban patch density and edge density. Through changes in these values a general temporal oscillation between phases of diffusion and coalescence in urban growth was revealed. Analysis of historical datasets revealed preliminary evidence supporting an addition to the theory of urban growth dynamics, one alluded to in some previous research, but not well developed. The empirical results and findings provide a lead for future research into the dynamics of urban growth and further development of existing urban theory. Numéro de notice : A2005-047 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658810410001713407 En ligne : https://doi.org/10.1080/13658810410001713407 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27185
in International journal of geographical information science IJGIS > vol 19 n° 2 (february 2005) . - pp 175 - 195[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-05021 RAB Revue Centre de documentation En réserve L003 Disponible 079-05022 RAB Revue Centre de documentation En réserve L003 Disponible Performance of different spectral and textural photograph features in multi-source forest inventory / Sakari Tuominen in Remote sensing of environment, vol 94 n° 2 (30/01/2005)PermalinkStatistique spatiale / Jean-Marc Zaninetti (2005)PermalinkDigital bathymetric models from rational profiles / R.M. Marin in Surveying and land information science, vol 64 n° 4 (01/12/2004)PermalinkMaritime aerosol optical thickness measured by handheld sun photometers / K.D. Knobelspiesse in Remote sensing of environment, vol 93 n° 1 (30/10/2004)PermalinkClassification of hyperspectral remote sensing images with support vector machines / F. Melgani in IEEE Transactions on geoscience and remote sensing, vol 42 n° 8 (August 2004)PermalinkClustering with obstacles for geographical data mining / V. Estivill-Castro in ISPRS Journal of photogrammetry and remote sensing, vol 59 n° 1-2 (August 2004 - April 2005)PermalinkA double continuous approach to visualization and analysis of categorial maps / T. Hengl in International journal of geographical information science IJGIS, vol 18 n° 2 (march 2004)PermalinkDelineation of forest/nonforest land use classes using nearest neighbor methods / R. Haapanen in Remote sensing of environment, vol 89 n° 3 (15/02/2004)PermalinkLinear features extraction in rain forest context from interferometric SAR images by fusion of coherence and amplitude information / V.P. Onana in IEEE Transactions on geoscience and remote sensing, vol 41 n° 11 (November 2003)PermalinkStrategies for integrating information from multiple resolutions into land-use/land-cover classification routines / D.M. Chen in Photogrammetric Engineering & Remote Sensing, PERS, vol 69 n° 11 (November 2003)Permalink