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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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Application de la classification floue (fuzzy k-NN) à l'étude de l'occupation du sol d'une zone urbaine : le cas de la région de Genève / S. Rakotoniaina in Photo interprétation, European journal of applied remote sensing, vol 46 n° 2 (juin 2010)
[article]
Titre : Application de la classification floue (fuzzy k-NN) à l'étude de l'occupation du sol d'une zone urbaine : le cas de la région de Genève Type de document : Article/Communication Auteurs : S. Rakotoniaina, Auteur ; Claude Collet, Auteur Année de publication : 2010 Article en page(s) : pp 66 - 73 Note générale : Bibliographie Langues : Français (fre) Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification barycentrique
[Termes IGN] classification floue
[Termes IGN] classification hybride
[Termes IGN] classification non dirigée
[Termes IGN] Genève
[Termes IGN] image SPOT XS
[Termes IGN] milieu urbainRésumé : (Auteur) La présence de pixels mixtes, dans le cas d'une zone urbaine par exemple, rend quelques fois difficile la classification d'images à l'aide des classificateurs classiques ou d'une approche rigide. L'utilisation de classificateurs flous trouve leur apport dans telles circonstances. Nous avons expérimenté l'approche floue de k-NN (fuzzy k-NN) dont la théorie a été développée par Keller et al. en 1985. Cette théorie combine l'approche floue avec le classificateur non-paramétrique k-NN, que nous avons déjà expérimenté avec succès dans le cadre de travaux antérieurs (Rakotoniaina et al., 2009 ; Rakotoniaina et Collet, 2010). Dans cet article, nous illustrons notre étude avec l'emploi d'une image SPOT-XS sur la région de Genève. Les résultats obtenus nous montrent l'intérêt de cette méthode floue non paramétrique par rapport à la méthode courante rigide du maximum de vraisemblance. Un gain en précision globale de plus de 19% a été observé. Numéro de notice : A2010-443 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30636
in Photo interprétation, European journal of applied remote sensing > vol 46 n° 2 (juin 2010) . - pp 66 - 73[article]Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité 104-2010021 SL Revue Centre de documentation Revues en salle Exclu du prêt Effects of topographic variability and Lidar sampling density on several DEM interpolation methods / Q. Guo in Photogrammetric Engineering & Remote Sensing, PERS, vol 76 n° 6 (June 2010)
[article]
Titre : Effects of topographic variability and Lidar sampling density on several DEM interpolation methods Type de document : Article/Communication Auteurs : Q. Guo, Auteur ; W. Li, Auteur ; H. Yu, Auteur ; O. Alvarez, Auteur Année de publication : 2010 Article en page(s) : pp 701 - 712 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] classification barycentrique
[Termes IGN] densité des points
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] fonction spline d'interpolation
[Termes IGN] interpolation
[Termes IGN] interpolation inversement proportionnelle à la distance
[Termes IGN] krigeage
[Termes IGN] modèle numérique de terrain
[Termes IGN] Triangulated Irregular Network
[Termes IGN] variabilitéRésumé : (Auteur) This study aims to quantify the effects of topographic variability (measured by coefficient variation of elevation, CV) and lidar (Light Detection and Ranging) sampling density on the DEM (Digital Elevation Model) accuracy derived from several interpolation methods at different spatial resolutions. Interpolation methods include natural neighbor (NN), inverse distance weighted (IDW), triangulated irregular network (TIN), spline, ordinary kriging (OK), and universal kriging (UK). This study is unique in that a comprehensive evaluation of the combined effects of three influencing factors (CV, sampling density, and spatial resolution) on lidar-derived DEM accuracy is carried out using different interpolation methods. Results indicate that simple interpolation methods, such as IDW, NN, and TIN, are more efficient at generating DEMs from lidar data, but kriging-based methods, such as OK and UK, are more reliable if accuracy is the most important consideration. Moreover, spatial resolution also plays an important role when generating DEMs from lidar data. Our results could be used to guide the choice of appropriate lidar interpolation methods for DEM generation given the resolution, sampling density, and topographic variability. Numéro de notice : A2010-228 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.76.6.701 En ligne : https://doi.org/10.14358/PERS.76.6.701 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30422
in Photogrammetric Engineering & Remote Sensing, PERS > vol 76 n° 6 (June 2010) . - pp 701 - 712[article]Algorithms for constrained k-nearest neighbor queries over moving object trajectories / Yunjun Gao in Geoinformatica, vol 14 n° 2 (April 2010)
[article]
Titre : Algorithms for constrained k-nearest neighbor queries over moving object trajectories Type de document : Article/Communication Auteurs : Yunjun Gao, Auteur ; B. Zheng, Auteur ; G. Chen, Auteur ; Qi Li, Auteur Année de publication : 2010 Article en page(s) : pp 241 - 276 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] arbre-R
[Termes IGN] base de données d'objets mobiles
[Termes IGN] base de données spatiotemporelles
[Termes IGN] classification barycentrique
[Termes IGN] objet mobile
[Termes IGN] processus spatial
[Termes IGN] programmation par contraintes
[Termes IGN] requête spatialeRésumé : (Auteur) An important query for spatio-temporal databases is to find nearest trajectories of moving objects. Existing work on this topic focuses on the closest trajectories in the whole data space. In this paper, we introduce and solve constrained k-nearest neighbor (CkNN) queries and historical continuous CkNN (HCCkNN) queries on R-tree-like structures storing historical information about moving object trajectories. Given a trajectory set D, a query object (point or trajectory) q, a temporal extent T, and a constrained region CR, (i) a CkNN query over trajectories retrieves from D within T, the k (? 1) trajectories that lie closest to q and intersect (or are enclosed by) CR; and (ii) an HCCkNN query on trajectories retrieves the constrained k nearest neighbors (CkNNs) of q at any time instance of T. We propose a suite of algorithms for processing CkNN queries and HCCkNN queries respectively, with different properties and advantages. In particular, we thoroughly investigate two types of CkNN queries, i.e., CkNNP and CkNNT, which are defined with respect to stationary query points and moving query trajectories, respectively; and two types of HCCkNN queries, namely, HCCkNNP and HCCkNNT, which are continuous counterparts of CkNNP and CkNNT, respectively. Our methods utilize an existing data-partitioning index for trajectory data (i.e., TB-tree) to achieve low I/O and CPU cost. Extensive experiments with both real and synthetic datasets demonstrate the performance of the proposed algorithms in terms of efficiency and scalability. Copyright Springer Numéro de notice : A2010-067 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1007/s10707-009-0084-5 Date de publication en ligne : 28/04/2009 En ligne : https://doi.org/10.1007/s10707-009-0084-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30263
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Code-barres Cote Support Localisation Section Disponibilité 057-2010021 RAB Revue Centre de documentation En réserve L003 Disponible Efficient evaluation of continuous spatio-temporal queries on moving objects whith uncertain velocity / Y. Huang in Geoinformatica, vol 14 n° 2 (April 2010)
[article]
Titre : Efficient evaluation of continuous spatio-temporal queries on moving objects whith uncertain velocity Type de document : Article/Communication Auteurs : Y. Huang, Auteur ; C. Lee, Auteur Année de publication : 2010 Article en page(s) : pp 163 - 200 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] classification barycentrique
[Termes IGN] distance euclidienne
[Termes IGN] objet mobile
[Termes IGN] requête continue
[Termes IGN] requête spatiotemporelle
[Termes IGN] vitesseRésumé : (Auteur) Continuous Range (CR) query and Continuous K-Nearest Neighbor (CKNN) query are two important types of spatio-temporal queries. Given a time interval [ts , te ] and a moving query object q, a CR query is to find the moving objects whose Euclidean distances to q are within a user-given distance at each time instant within [ts , te ]. A CKNN query is to retrieve the K-Nearest Neighbors (KNNs) of this query object q at each time instant within [ts , te ]. In this paper, we investigate how to process these spatio-temporal queries efficiently under the situation that the velocity of each object is not fixed. This uncertainty on the velocity of object inevitably results in high complexity in processing spatio-temporal queries. We will discuss the complications incurred by this uncertainty and propose two algorithms, namely the Possibility-based possible within objects searching algorithm and the Possibility-based possible KNN searching algorithm, for the CR query and the CKNN query, respectively. A Possibility-based model is designed accordingly to quantify the possibility of each object being the result of a CR query or a CKNN query. Comprehensive experiments are performed to demonstrate the effectiveness and the efficiency of the proposed approaches. Copyright Springer Numéro de notice : A2010-064 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1007/s10707-009-0081-8 Date de publication en ligne : 23/04/2009 En ligne : https://doi.org/10.1007/s10707-009-0081-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30260
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Code-barres Cote Support Localisation Section Disponibilité 057-2010021 RAB Revue Centre de documentation En réserve L003 Disponible Forest object-oriented classification with customized and automatic attribute selection / Olivier de Joinville (2010)
Titre : Forest object-oriented classification with customized and automatic attribute selection Type de document : Article/Communication Auteurs : Olivier de Joinville , Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2010 Collection : International Archives of Photogrammetry and Remote Sensing, ISSN 0252-8231 num. 38-8 Conférence : ISPRS 2010, Technical Commission 8 Symposium, Networking the World with Remote Sensing 02/08/2010 12/08/2010 Kyoto Japon ISPRS OA Archives Commission 8 Importance : pp 669 - 674 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification barycentrique
[Termes IGN] classification orientée objet
[Termes IGN] composition colorée
[Termes IGN] forêt domaniale
[Termes IGN] image SPOT
[Termes IGN] matrice de confusion
[Termes IGN] segmentation d'image
[Termes IGN] Somme (80)Résumé : (auteur) This paper presents a semi-automatic method to optimize object-oriented classification without photointerpretation. The thematic studied is the forest (Crecy forest in the north of France). A SPOT 2 image at 20 m spatial resolution was analysed in a near infrared colour composite (green, red and infrared). New classification methods no longer work with pixels, but with regions derived from the previously segmented image [TRIAS 2006], [BENCHERIF 2009].The first step consists in image segmentation based on several criteria, a scale parameter and an homogeneity factor made up of two complementary factors: shape and radiometry. Two segmentations have been computed: one at very large scale (no more than 20 regions) in order to establish a manually made classification with only 2 classes: forest and no forest (this latter will not be classified). Another one at a smaller scale which will be used to select the test samples (also called training area) on the forest area. Once both segmentations and manual classification are completed and validated (essentially visually), the objective of this study is to determine semi automatically the most adapted attributes for each training area (5 training areas have been selected). Therefore, for all selected training areas, attributes are automatically selected, consecutively based on three criteria: radiometry, shape and texture. For each of these criteria, a maximum number of attributes is fixed among all potentially interesting attributes and the optimum attribute combination is automatically selected with respect to a statistical parameter derived from a distance matrix. The distance matrix optimizes the separation between the training areas. Then, 3 classifications were set up, each of them with the optimum automatically selected attribute combination derived from the previous step. For each of these classifications, a confusion matrix will be computed. For each training area its confusion rate with other training areas was computed and the lowest confusion rate was selected as the criterion. For instance, if there is a training area which has 35 % of confusion pixels with other classes for a radiometric combination, 25% for a textural combination and 5 % for a morphologic one (shape criterion), this training area will be affected with a morphologic attribute combination. The result is thus a new classification with the new customized attributes for each training area. In the assessment of this classification, the confusion rate for each class decreases significantly. Then, reliability maps are built to determine the risk of confusion between the classes. Test results are so far encouraging. Due to this new method, the confusion rates decrease significantly with respect to a standard nearest neighbour approach. Numéro de notice : C2010-032 Affiliation des auteurs : ENSG (1941-2011) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : sans En ligne : http://www.isprs.org/proceedings/XXXVIII/part8/pdf/W07P02_20100218000017.pdf Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90642 Documents numériques
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Forest object-oriented classification ... - pdf éditeurAdobe Acrobat PDF Location-based algorithms for finding sets of corresponding objects over several geo-spatial data sets / E. Safra in International journal of geographical information science IJGIS, vol 24 n°1-2 (january 2010)PermalinkWeb data retrieval: solving spatial range queries using k-nearest neighbor searches / W. Bae in Geoinformatica, vol 13 n° 4 (December 2009)PermalinkA matching algorithm for detecting land use changes using case-based reasoning / X. Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 75 n° 11 (November 2009)PermalinkEvaluating AISA+ hyperspectral imagery for mapping black mangrove along the South Texas gulf coast / C. Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 75 n° 4 (April 2009)PermalinkContinuous K-nearest neighbor query for moving objects with uncertain velocity / Y. Huang in Geoinformatica, vol 13 n° 1 (March 2009)PermalinkPan-European forest/non forest mapping with Landsat ETM+ and Corine Land Cover 2000 data / A. Pekkarinen in ISPRS Journal of photogrammetry and remote sensing, vol 64 n° 2 (March - April 2009)PermalinkQuantifying indicators of riparian condition in Australian tropical savannas: integrating high spatial resolution imagery and field survey data / K. Johansen in International Journal of Remote Sensing IJRS, vol 29 n°23 - 24 (December 2008)PermalinkA standardized probability comparison approach for evaluating and combining pixel-based classification procedures / D. Chen in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 5 (May 2008)PermalinkWeighting function alternatives for a subpixel allocation model / Y. Makido in Photogrammetric Engineering & Remote Sensing, PERS, vol 73 n° 11 (November 2007)PermalinkAccuracy of forest mapping based on Landsat TM data and a kNN-based method / K. Gjertsen in Remote sensing of environment, vol 110 n° 4 (30/10/2007)Permalink