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Parametric, bootstrap, and jackknife variance estimators for the k-Nearest Neighbors technique with illustrations using forest inventory and satellite image data / Ronald E. McRoberts in Remote sensing of environment, vol 115 n° 12 (december 2011)  

Public [article]  inRemote sensing of environment > vol 115 n° 12 (december 2011) . - pp 3165 - 3174 Titre : Parametric, bootstrap, and jackknife variance estimators for the k-Nearest Neighbors technique with illustrations using forest inventory and satellite image data Type de document : Article/Communication Auteurs : Ronald E. McRoberts, Auteur ; Magnussen, Steen, Auteur ; Erkki Tomppo, Auteur ; Gherardo Chirici, Auteur Année de publication : 2011 Article en page(s) : pp 3165 - 3174 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes IGN] classification barycentrique [Termes IGN] Finlande [Termes IGN] image Landsat [Termes IGN] inférence statistique [Termes IGN] inventaire forestier étranger (données) [Termes IGN] regroupement de données Résumé : (auteur) Nearest neighbors techniques have been shown to be useful for estimating forest attributes, particularly when used with forest inventory and satellite image data. Published reports of positive results have been truly international in scope. However, for these techniques to be more useful, they must be able to contribute to scientific inference which, for sample-based methods, requires estimates of uncertainty in the form of variances or standard errors. Several parametric approaches to estimating uncertainty for nearest neighbors techniques have been proposed, but they are complex and computationally intensive. For this study, two resampling estimators, the bootstrap and the jackknife, were investigated and compared to a parametric estimator for estimating uncertainty using the k-Nearest Neighbors (k-NN) technique with forest inventory and Landsat data from Finland, Italy, and the USA. The technical objectives of the study were threefold: (1) to evaluate the assumptions underlying a parametric approach to estimating k-NN variances; (2) to assess the utility of the bootstrap and jackknife methods with respect to the quality of variance estimates, ease of implementation, and computational intensity; and (3) to investigate adaptation of resampling methods to accommodate cluster sampling. The general conclusions were that support was provided for the assumptions underlying the parametric approach, the parametric and resampling estimators produced comparable variance estimates, care must be taken to ensure that bootstrap resampling mimics the original sampling, and the bootstrap procedure is a viable approach to variance estimation for nearest neighbor techniques that use very small numbers of neighbors to calculate predictions. Numéro de notice : A2011-610 Affiliation des auteurs : non IGN Thématique : FORET/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2011.07.002 Date de publication en ligne : 27/08/2011 En ligne : https://doi.org/10.1016/j.rse.2011.07.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78400 [article]

Automatic fuzzy clustering using modified differential evolution for image classification / U. Maulik in IEEE Transactions on geoscience and remote sensing, vol 48 n° 9 (September 2010)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 48 n° 9 (September 2010) . - pp 3503 - 3510 Titre : Automatic fuzzy clustering using modified differential evolution for image classification Type de document : Article/Communication Auteurs : U. Maulik, Auteur ; I. Saha, Auteur Année de publication : 2010 Article en page(s) : pp 3503 - 3510 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] algorithme génétique [Termes IGN] classification floue [Termes IGN] classification non dirigée [Termes IGN] identification automatique [Termes IGN] image satellite [Termes IGN] occupation du sol [Termes IGN] regroupement de données Résumé : (Auteur) The problem of classifying an image into different homogeneous regions is viewed as the task of clustering the pixels in the intensity space. In particular, satellite images contain landcover types, some of which cover significantly large areas while some (e.g., bridges and roads) occupy relatively much smaller regions. Automatically detecting regions or clusters of such widely varying sizes is a challenging task. In this paper, a new real-coded modified differential evolution based automatic fuzzy clustering algorithm is proposed which automatically evolves the number of clusters as well as the proper partitioning from a data set. Here, the assignment of points to different clusters is done based on a Xie-Beni index where the Euclidean distance is taken into consideration. The effectiveness of the proposed technique is first demonstrated for two numeric remote sensing data described in terms of feature vectors and then in identifying different landcover regions in remote sensing imagery. The superiority of the new method is demonstrated by comparing it with other existing techniques like automatic clustering using improved differential evolution, classical differential evolution based automatic fuzzy clustering, variable length genetic algorithm based fuzzy clustering, and well known fuzzy C-means algorithm both qualitatively and quantitatively. Numéro de notice : A2010-571 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2010.2047020 En ligne : https://ieeexplore.ieee.org/document/5462924 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30762 [article]

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Using clustering methods in geospatial information systems / X. Wang in Geomatica, vol 64 n° 3 (September 2010)  

Public [article]  inGeomatica > vol 64 n° 3 (September 2010) . - pp 347 - 361 Titre : Using clustering methods in geospatial information systems Type de document : Article/Communication Auteurs : X. Wang, Auteur ; Jing Wang, Auteur Année de publication : 2010 Article en page(s) : pp 347 - 361 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] analyse spatiale [Termes IGN] attribut sémantique [Termes IGN] distance euclidienne [Termes IGN] exploration de données géographiques [Termes IGN] modèle conceptuel de données localisées [Termes IGN] regroupement de données [Termes IGN] système d'information géographique [Termes IGN] test de performance Résumé : (Auteur) Spatial clustering is the process of grouping similar objects based on their distance, connectivity, or rel-ative density in space. It has been employed in the field of spatial analysis for years. In order to select the prop-er spatial clustering methods for geospatial information systems, we need to consider the characteristics of different clustering methods, relative to the objectives that we are trying to achieve. In this paper, we give a detailed discussion of different types of clustering methods from a data mining perspective. Analysis of the advantages and limitations of some classical clustering methods are given. Subsequently we discuss applying spatial clustering methods as part of geospatial information systems, with respect to distance functions, data models, non-spatial attributes and performance. Numéro de notice : A2010-529 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.5623/geomat-2010-0035 En ligne : https://doi.org/10.5623/geomat-2010-0035 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30721 [article]

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Semantic-based pruning of redundant and uninteresting frequent geographic patterns / Vania Bogorny in Geoinformatica, vol 14 n° 2 (April 2010)  

Public [article]  inGeoinformatica > vol 14 n° 2 (April 2010) . - pp 201 - 220 Titre : Semantic-based pruning of redundant and uninteresting frequent geographic patterns Type de document : Article/Communication Auteurs : Vania Bogorny, Auteur ; J. Valiati, Auteur ; L. Alvares, Auteur Année de publication : 2010 Article en page(s) : pp 201 - 220 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] base de données localisées [Termes IGN] exploration de données géographiques [Termes IGN] modèle sémantique de données [Termes IGN] regroupement de données [Termes IGN] sémantique Résumé : (Auteur) In geographic association rule mining many patterns are either redundant or contain well known geographic domain associations explicitly represented in knowledge resources such as geographic database schemas and geo-ontologies. Existing spatial association rule mining algorithms are Apriori-like, and therefore generate a large amount of redundant patterns. For non-spatial data, the closed frequent pattern mining technique has been introduced to remove redundant patterns. This approach, however, does not warrant the elimination of both redundant and well known geographic dependences when mining geographic databases. This paper presents a novel method for pruning both redundant and well known geographic dependences, by pushing semantics into the pattern mining task. Experiments with real geographic databases have demonstrated a significant reduction of the total amount of patterns and the efficiency of the method. Copyright Springer Numéro de notice : A2010-065 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-009-0082-7 Date de publication en ligne : 06/05/2009 En ligne : https://doi.org/10.1007/s10707-009-0082-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30261 [article]

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Delineation and geometric modeling of road networks / C. Poullis in ISPRS Journal of photogrammetry and remote sensing, vol 65 n° 2 (March - April 2010)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 65 n° 2 (March - April 2010) . - pp 165 - 181 Titre : Delineation and geometric modeling of road networks Type de document : Article/Communication Auteurs : C. Poullis, Auteur ; S. You, Auteur Année de publication : 2010 Article en page(s) : pp 165 - 181 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] arc [Termes IGN] axe médian [Termes IGN] données lidar [Termes IGN] extraction automatique [Termes IGN] filtre de Gabor [Termes IGN] image satellite [Termes IGN] photographie aérienne [Termes IGN] regroupement de données [Termes IGN] réseau routier Résumé : (Auteur) In this work we present a novel vision-based system for automatic detection and extraction of complex road networks from various sensor resources such as aerial photographs, satellite images, and LiDAR. Uniquely, the proposed system is an integrated solution that merges the power of perceptual grouping theory (Gabor filtering, tensor voting) and optimized segmentation techniques (global optimization using graph-cuts) into a unified framework to address the challenging problems of geospatial feature detection and classification. Firstly, the local precision of the Gabor filters is combined with the global context of the tensor voting to produce accurate classification of the geospatial features. In addition, the tensorial representation used for the encoding of the data eliminates the need for any thresholds, therefore removing any data dependencies. Secondly, a novel orientation-based segmentation is presented which incorporates the classification of the perceptual grouping, and results in segmentations with better defined boundaries and continuous linear segments. Finally, a set of gaussian-based filters are applied to automatically extract centerline information (magnitude, width and orientation). This information is then used for creating road segments and transforming them to their polygonal representations. Numéro de notice : A2010-091 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2009.10.004 En ligne : https://doi.org/10.1016/j.isprsjprs.2009.10.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30287 [article]

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Regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP) / D. Guo in International journal of geographical information science IJGIS, vol 22 n° 6-7 (june 2008)  

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Designing visual analytics methods for massive collections of movement data / Natalia Adrienko in Cartographica, vol 42 n° 2 (June 2007)  

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A new approach to the nearest-neighbour method to discover cluster features in overlaid spatial point processes / Tao Pei in International journal of geographical information science IJGIS, vol 20 n° 2 (february 2006)  

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Clustérisation des calculs quotidiens du réseau GPS permanent, Volume 1. Rapport de stage / Yannick Carré (2005)

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Data-gathering strategies for social-behavioural research about participatory geographical information system use / T. Nyerges in International journal of geographical information science IJGIS, vol 16 n° 1 (january 2002)  

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Efficient polygon amalgamation methods for spatial OLAP and spatial data mining / X. Zhou (20/07/1999)  

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Generalizing graphs using amalgamation and selection / J.G. Stell (20/07/1999)  

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Recognition of building clusters for generalization / Nicolas Regnauld (12/08/1996)

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