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Social Distance metric: from coordinates to neighborhoods / Vagan Terziyan in International journal of geographical information science IJGIS, vol 31 n° 11-12 (November - December 2017)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 31 n° 11-12 (November - December 2017) . - pp 2401 - 2426 Titre : Social Distance metric: from coordinates to neighborhoods Type de document : Article/Communication Auteurs : Vagan Terziyan, Auteur Année de publication : 2017 Article en page(s) : pp 2401 - 2426 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] base de données localisées [Termes IGN] classification [Termes IGN] distance [Termes IGN] exploration de données géographiques [Termes IGN] géographie sociale [Termes IGN] interpolation [Termes IGN] métrique [Termes IGN] plus proche voisin, algorithme du [Termes IGN] système d'information géographique [Termes IGN] voisinage (relation topologique) Résumé : (Auteur) Choice of a distance metric is a key for the success in many machine learning and data processing tasks. The distance between two data samples traditionally depends on the values of their attributes (coordinates) in a data space. Some metrics also take into account the distribution of samples within the space (e.g. local densities) aiming to improve potential classification or clustering performance. In this paper, we suggest the Social Distance metric that can be used on top of any traditional metric. For a pair of samples x and y, it averages the two numbers: the place (rank), which sample y holds in the list of ordered nearest neighbors of x; and vice versa, the rank of x in the list of the nearest neighbors of y. Average is a contraharmonic Lehmer mean, which penalizes the difference between the numbers by giving values greater than the Arithmetic mean for the unequal arguments. We consider normalized average as a distance function and we prove it to be a metric. We present several modifications of such metric and show that their properties are useful for a variety of classification and clustering tasks in data spaces or graphs in a Geographic Information Systems context and beyond. Numéro de notice : A2017-701 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1367796 En ligne : https://doi.org/10.1080/13658816.2017.1367796 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88082 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2017061	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-2017062	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Learning a discriminative distance metric with label consistency for scene classification / Yuebin Wang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 8 (August 2017) . - pp 4427 - 4440 Titre : Learning a discriminative distance metric with label consistency for scene classification Type de document : Article/Communication Auteurs : Yuebin Wang, Auteur ; Liqiang Zhang, Auteur ; Hao Deng, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 4427 - 4440 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme d'apprentissage [Termes IGN] apprentissage dirigé [Termes IGN] image hyperspectrale [Termes IGN] métrique [Termes IGN] précision de la classification Résumé : (Auteur) To achieve high scene classification performance of high spatial resolution remote sensing images (HSR-RSIs), it is important to learn a discriminative space in which the distance metric can precisely measure both similarity and dissimilarity of features and labels between images. While the traditional metric learning methods focus on preserving interclass separability, label consistency (LC) is less involved, and this might degrade scene images classification accuracy. Aiming at considering intraclass compactness in HSR-RSIs, we propose a discriminative distance metric learning method with LC (DDML-LC). The DDML-LC starts from the dense scale invariant feature transformation features extracted from HSR-RSIs, and then uses spatial pyramid maximum pooling with sparse coding to encode the features. In the learning process, the intraclass compactness and interclass separability are enforced while the global and local LC after the feature transformation is constrained, leading to a joint optimization of feature manifold, distance metric, and label distribution. The learned metric space can scale to discriminate out-of-sample HSR-RSIs that do not appear in the metric learning process. Experimental results on three data sets demonstrate the superior performance of the DDML-LC over state-of-the-art techniques in HSR-RSI classification. Numéro de notice : A2017-498 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2692280 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2692280 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86440 [article]

Geographically weighted regression with parameter-specific distance metrics / Binbin Lu in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 31 n° 5-6 (May-June 2017) . - pp 982 - 998 Titre : Geographically weighted regression with parameter-specific distance metrics Type de document : Article/Communication Auteurs : Binbin Lu, Auteur ; Chris Brunsdon, Auteur ; Martin Charlton, Auteur ; Paul Harris, Auteur Année de publication : 2017 Article en page(s) : pp 982 - 998 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] anisotropie [Termes IGN] métrique [Termes IGN] régression géographiquement pondérée [Termes IGN] relation spatiale Résumé : (auteur) Geographically weighted regression (GWR) is an important local technique to model spatially varying relationships. A single distance metric (Euclidean or non-Euclidean) is generally used to calibrate a standard GWR model. However, variations in spatial relationships within a GWR model might also vary in intensity with respect to location and direction. This assertion has led to extensions of the standard GWR model to mixed (or semiparametric) GWR and to flexible bandwidth GWR models. In this article, we present a strongly related extension in fitting a GWR model with parameter-specific distance metrics (PSDM GWR). As with mixed and flexible bandwidth GWR models, a back-fitting algorithm is used for the calibration of the PSDM GWR model. The value of this new GWR model is demonstrated using a London house price data set as a case study. The results indicate that the PSDM GWR model can clearly improve the model calibration in terms of both goodness of fit and prediction accuracy, in contrast to the model fits when only one metric is singly used. Moreover, the PSDM GWR model provides added value in understanding how a regression model’s relationships may vary at different spatial scales, according to the bandwidths and distance metrics selected. PSDM GWR deals with spatial heterogeneities in data relationships in a general way, although questions remain on its model diagnostics, distance metric specification, and computational efficiency, providing options for further research. Numéro de notice : A2017-238 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2016.1263731 En ligne : http://dx.doi.org/10.1080/13658816.2016.1263731 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85172 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2017031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

The analysis and measurement of building patterns using texton co-occurrence matrices / Wenhao Yu in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 31 n° 5-6 (May-June 2017) Titre : The analysis and measurement of building patterns using texton co-occurrence matrices Type de document : Article/Communication Auteurs : Wenhao Yu, Auteur ; Tinghua Ai, Auteur ; Pengcheng Liu, Auteur ; Xiaoqiang Cheng, Auteur Année de publication : 2017 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] données vectorielles [Termes IGN] matrice de co-occurrence [Termes IGN] métrique [Termes IGN] modèle géométrique du bâti [Termes IGN] reconnaissance de formes [Termes IGN] reconstruction 2D du bâti [Termes IGN] tessellation [Termes IGN] triangulation de Delaunay [Termes IGN] voisinage (relation topologique) Résumé : (auteur) The representation and analysis of building patterns are critical for characterizing urban scenes and making decisions in urban planning. The evaluation of building patterns is a difficult spatial analysis problem that exhibits properties of symbolization, homogeneity and regularity. Open issues in this field include the development of approaches for representing building patterns and vector-based methods for computing various pattern metrics. In the image analysis domain, there are many methods for pattern recognition (e.g., texture analysis), but there are few corresponding solutions for vector data. The aim of this research is to develop several building pattern metrics and offer a texton co-occurrence matrix (TCM)-based method to quantitatively evaluate the features of building patterns. The procedure first constructs a spatial field based on a Delaunay triangulation skeleton to partition a set of buildings into a set of tessellation cells. The tessellations of building clusters have a similar structure as image representations, in that each cell corresponds to an image pixel. We then use the texton analysis to establish a matrix to describe the tessellation structure, including the neighboring relationships and individual attribute information. Finally, a set of feature descriptors is obtained from the TCM to capture the texture-related information of building groups. Through experiments on building pattern analysis and spatial queries, we show that the results of TCM-based evaluation of building patterns are consistent with those of human cognition. Numéro de notice : A2017-242 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2016.1265121 En ligne : http://dx.doi.org/10.1080/13658816.2016.1265121 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85178 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2017031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Implications of weighting metrics for line generalization with Visvalingam's algorithm / Mahes Visvalingam in Cartographic journal (the), Vol 53 n° 3 (August 2016)  

Public [article]  inCartographic journal (the) > Vol 53 n° 3 (August 2016) . - pp 253 - 267 Titre : Implications of weighting metrics for line generalization with Visvalingam's algorithm Type de document : Article/Communication Auteurs : Mahes Visvalingam, Auteur ; J.C. Whelan, Auteur Année de publication : 2016 Article en page(s) : pp 253 - 267 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] algorithme de Visvalingam [Termes IGN] littoral [Termes IGN] métrique [Termes IGN] pondération [Termes IGN] retour d'expérience technique [Termes IGN] simplification de contour [Vedettes matières IGN] Généralisation Résumé : (auteur) Visvalingam's algorithm was designed for caricatural line generalization. A distinction must be made between the algorithm and its operational definition, which includes the metric used to drive it. When the algorithm was first introduced, it was demonstrated using the concept of the effective area of triangles. It was noted that alternative metrics could be used and that the metrics could be weighted, for example to take account of shape. Ordnance Survey (Great Britain) and others are using Visvalingam's algorithm for generalizing coastlines and other natural features, with complex parameter-driven functions to weight the original metric. This paper shows how free software and data were used to scrutinize the implications of one of Matthew Bloch's simple and transparent weighting functions. The results look promising, when compared with manually produced mid and small-scale maps; and encourage further research focussed on weighting functions and related topics, such as self-intersection of lines and model-based generalization. The paper discusses why weights were used in some projects. It comments on their range of applicability and reiterates the original guidance provided for the use of weights. It also demonstrates how weights can undermine the algorithm's capacity to draw caricatures with very few points. The paper provides sufficient background and links to the authors’ test data and to open source software for the benefit of others wishing to undertake research in line generalization using Visvalingam's algorithm. Numéro de notice : A2016-682 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00087041.2016.1149906 En ligne : http://dx.doi.org/10.1080/00087041.2016.1149906 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81942 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
030-2016031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

The Visvalingam algorithm: metrics, measures and heuristics / Mahes Visvalingam in Cartographic journal (the), Vol 53 n° 3 (August 2016)  

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On the interest of penetration depth, canopy area and volume metrics to improve Lidar-based models of forest parameters / Cédric Vega in Remote sensing of environment, vol 175 (15 March 2016)  

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Irregular variations in GPS time series by probability and noise analysis / Anna Klos in Survey review, vol 47 n° 342 (May 2015)  

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Protecting query privacy in location-based services / Xihui Chen in Geoinformatica, vol 18 n° 1 (January 2014)  

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Landscape metrics for analysing urbanization-induced land use and land cover changes / Hua Liu in Geocarto international, vol 28 n° 7-8 (November - December 2013)  

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La modélisation des réseaux écologiques par les graphes paysagers : Méthodes et outils / Jean-Christophe Foltête in Revue internationale de géomatique, vol 22 n° 4 (décembre 2012 – février 2013)  

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Mapping fragmented agricultural systems in the Sudano-Sahelian environments of Africa using random forest and ensemble metrics of coarse resolution MODIS imagery / E. Vintrou in Photogrammetric Engineering & Remote Sensing, PERS, vol 78 n° 8 (August 2012)  

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Estimating forest attribute parameters for small areas using nearest neighbors techniques / Ronald E. McRoberts in Forest ecology and management, vol 272 (mai 2012)  

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Using landscape characteristics to define an adjusted distance metric for improving kriging interpolations / S. Lyon in International journal of geographical information science IJGIS, vol 24 n° 5-6 (may 2010)  

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Automated conflation of digital gazetteer data / J.T. Hastings in International journal of geographical information science IJGIS, vol 22 n° 10 (october 2008)  

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