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Point grid map : a new type of thematic map for statistical data associated with geographic points / Mengjie Zhou in Cartography and Geographic Information Science, Vol 44 n° 5 (September 2017)  

Public [article]  inCartography and Geographic Information Science > Vol 44 n° 5 (September 2017) . - pp 374 - 389 Titre : Point grid map : a new type of thematic map for statistical data associated with geographic points Type de document : Article/Communication Auteurs : Mengjie Zhou, Auteur ; Jing Tian, Auteur ; Fuquan Xiong, Auteur ; Rui Wang, Auteur Année de publication : 2017 Article en page(s) : pp 374 - 389 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes IGN] cartographie statistique [Termes IGN] diagramme [Termes IGN] données socio-économiques [Termes IGN] grille [Termes IGN] point [Termes IGN] représentation cartographique [Termes IGN] symbole graphique Résumé : (Auteur) Social, economic, and environmental statistical data associated with geographic points are currently globally available in large amounts. When conventional thematic maps, such as proportional symbol maps or point diagram maps, are used to represent these data, the maps appear cluttered if the point data volumes are relatively large or cover a relatively dense region. To overcome these limitations, we propose a new type of thematic map for statistical data associated with geographic points: the point grid map. In a point grid map, an input point data set is transformed into a grid in which each point is represented by a square grid cell of equal size while preserving the relative position of each point, which leads to a clear and uncluttered appearance, and the grid cells can be shaded or patterned with symbols or diagrams according to the attributes of the points. We present an algorithm to construct a point grid map and test it with several simulated and real data sets. Furthermore, we present some variants of the point grid map. Numéro de notice : A2017-447 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2016.1160797 En ligne : http://dx.doi.org/10.1080/15230406.2016.1160797 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86355 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
032-2017051	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Grid pattern recognition in road networks using the C4.5 algorithm / Jing Tian in Cartography and Geographic Information Science, Vol 43 n° 3 (June 2016)  

Public [article]  inCartography and Geographic Information Science > Vol 43 n° 3 (June 2016) . - pp 266 - 282 Titre : Grid pattern recognition in road networks using the C4.5 algorithm Type de document : Article/Communication Auteurs : Jing Tian, Auteur ; Zihan Song, Auteur ; Fei Gao, Auteur ; Feng Zhao, Auteur Année de publication : 2016 Article en page(s) : pp 266 - 282 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] apprentissage dirigé [Termes IGN] classification dirigée [Termes IGN] exploration de données géographiques [Termes IGN] grille [Termes IGN] reconnaissance de formes [Termes IGN] réseau routier Résumé : (Auteur) Pattern recognition in road networks can be used for different applications, including spatiotemporal data mining, automated map generalization, data matching of different levels of detail, and other important research topics. Grid patterns are a common pattern type. This paper proposes and implements a method for grid pattern recognition based on the idea of mesh classification through a supervised learning process. To train the classifier, training datasets are selected from worldwide city samples with different cultural, historical, and geographical environments. Meshes are subsequently labeled as composing or noncomposing grids by participants in an experiment, and the mesh measures are defined while accounting for the mesh’s individual characteristics and spatial context. The classifier is generated using the C4.5 algorithm. The accuracy of the classifier is evaluated using Kappa statistics and the overall rate of correctness. The average Kappa value is approximately 0.74, which corresponds to a total accuracy of 87.5%. Additionally, the rationality of the classifier is evaluated in an interpretation step. Two other existing grid pattern recognition methods were also tested on the datasets, and comparison results indicate that our approach is effective in identifying grid patterns in road networks. Numéro de notice : A2016-167 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/15230406.2015.1062425 En ligne : https://doi.org/10.1080/15230406.2015.1062425 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80473 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
032-2016031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Detection and correction of inconsistencies between river networks and contour data by spatial constraint knowledge / Tinghua Ai in Cartography and Geographic Information Science, Vol 42 n° 1 (January 2015)  

Public [article]  inCartography and Geographic Information Science > Vol 42 n° 1 (January 2015) . - pp 79 - 93 Titre : Detection and correction of inconsistencies between river networks and contour data by spatial constraint knowledge Type de document : Article/Communication Auteurs : Tinghua Ai, Auteur ; Min Yang, Auteur ; Xiang Zhang, Auteur ; Jing Tian, Auteur Année de publication : 2015 Article en page(s) : pp 79 - 93 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] cohérence des données [Termes IGN] programmation par contraintes [Termes IGN] réseau fluvial Résumé : (auteur) In the representation of topographic data, the distribution of hydrographic networks should be constrained by the contour model’s landform features. During the integration of topographic databases, however, spatial conflicts may destroy these constraints, generating inconsistencies. This study presents a method to detect and correct inconsistencies between river networks and contour data by spatial knowledge. First, structured terrain features are extracted from the contour-based geometric representation and matching relationships between rivers and contours are constructed based on spatial knowledge of the distribution of rivers and talwegs. We then propose a distance metric for measuring differences and identifying inconsistencies between the matched river and contour features. Three correction approaches are provided for different inconsistency situations, including river adjustment referenced to the contour, contour adjustment referenced to the river and adjustment of both river and contour to middle positions. We apply the proposed method to the integration and maintenance of national topographic infrastructure in order to demonstrate its effectiveness. Numéro de notice : A2015-235 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2014.956673 En ligne : https://doi.org/10.1080/15230406.2014.956673 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76228 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
032-2015011	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Region-based automatic building and forest change detection on Cartosat-1 stereo imagery / Jing Tian in ISPRS Journal of photogrammetry and remote sensing, vol 79 (May 2013)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 79 (May 2013) . - pp 226 - 239 Titre : Region-based automatic building and forest change detection on Cartosat-1 stereo imagery Type de document : Article/Communication Auteurs : Jing Tian, Auteur ; Peter Reinartz, Auteur ; Pablo d' Angelo, Auteur ; Manfred Ehlers, Auteur Année de publication : 2013 Article en page(s) : pp 226 - 239 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse diachronique [Termes IGN] bâtiment industriel [Termes IGN] détection de changement [Termes IGN] fonction régionalisée [Termes IGN] forêt [Termes IGN] image Cartosat-1 [Termes IGN] image panchromatique [Termes IGN] modèle numérique de surface [Termes IGN] orthorectification Résumé : (Auteur) In this paper a novel region-based method is proposed for change detection using space borne panchromatic Cartosat-1 stereo imagery. In the first step, Digital Surface Models (DSMs) from two dates are generated by semi-global matching. The geometric lateral resolution of the DSMs is 5 m x 5 m and the height accuracy is in the range of approximately 3 m (RMSE). In the second step, mean-shift segmentation is applied on the orthorectified images of two dates to obtain initial regions. A region intersection following a merging strategy is proposed to get minimum change regions and multi-level change vectors are extracted for these regions. Finally change detection is achieved by combining these features with weighted change vector analysis. The result evaluations demonstrate that the applied DSM generation method is well suited for Cartosat-1 imagery, and the extracted height values can largely improve the change detection accuracy, moreover it is shown that the proposed change detection method can be used robustly for both forest and industrial areas. Numéro de notice : A2013-239 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.02.017 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.02.017 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32377 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2013051	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Local manifold learning-based k-Nearest-Neighbor for hyperspectral image classification / Li Ma in IEEE Transactions on geoscience and remote sensing, vol 48 n° 11 (November 2010)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 48 n° 11 (November 2010) . - pp 1099 - 4109 Titre : Local manifold learning-based k-Nearest-Neighbor for hyperspectral image classification Type de document : Article/Communication Auteurs : Li Ma, Auteur ; Jing Tian, Auteur Année de publication : 2010 Article en page(s) : pp 1099 - 4109 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage automatique [Termes IGN] classification barycentrique [Termes IGN] image AVIRIS [Termes IGN] image EO1-Hyperion [Termes IGN] image hyperspectrale Résumé : (Auteur) Approaches to combine local manifold learning (LML) and the k -nearest-neighbor (kNN) classifier are investigated for hyperspectral image classification. Based on supervised LML (SLML) and kNN, a new SLML-weighted kNN (SLML-W kNN) classifier is proposed. This method is appealing as it does not require dimensionality reduction and only depends on the weights provided by the kernel function of the specific ML method. Performance of the proposed classifier is compared to that of unsupervised LML (ULML) and SLML for dimensionality reduction in conjunction with the kNN (ULML- kNN and SLML-k NN). Three LML methods, locally linear embedding (LLE), local tangent space alignment (LTSA), and Laplacian eigenmaps, are investigated with these classifiers. In experiments with Hyperion and AVIRIS hyperspectral data, the proposed SLML-WkNN performed better than ULML- kNN and SLML-k NN, and the highest accuracies were obtained using weights provided by supervised LTSA and LLE. Numéro de notice : A2010-479 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2010.2055876 Date de publication en ligne : 23/08/2010 En ligne : https://doi.org/10.1109/TGRS.2010.2055876 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30672 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2010111	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Optimization in multi-scale segmentation of high-resolution satellite images for artificial feature recognition / Jing Tian in International Journal of Remote Sensing IJRS, vol 28 n°19-20 (October 2007)  

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