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Optimizing the spatial resolution of WorldView-2 imagery for discriminating forest vegetation at subspecies level in KwaZulu-Natal, South Africa / Romano Lottering in Geocarto international, vol 31 n° 7 - 8 (July - August 2016)
[article]
Titre : Optimizing the spatial resolution of WorldView-2 imagery for discriminating forest vegetation at subspecies level in KwaZulu-Natal, South Africa Type de document : Article/Communication Auteurs : Romano Lottering, Auteur ; Onisimo Mutanga, Auteur Année de publication : 2016 Article en page(s) : pp 870 - 880 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Afrique du sud (état)
[Termes IGN] classification dirigée
[Termes IGN] espèce végétale
[Termes IGN] image Worldview
[Termes IGN] pouvoir de résolution géométrique
[Termes IGN] rééchantillonnage
[Termes IGN] sous-étage
[Termes IGN] surface forestière
[Termes IGN] varianceRésumé : (Auteur) The objective of this study was to identify an appropriate spatial resolution for discriminating forest vegetation at subspecies level. WorldView-2 imagery was progressively resampled to coarser spatial resolutions. At a compartment level, 30 × 30-m subsets were generated across forest compartments to represent the five forest subspecies investigated in this study. From the centre of each subset, the spatial resolution of the original WorldView-2 image was resampled from 6 to 34-m, with increments of 4-m. The variance was then calculated at every resampled spatial resolution using each of the eight WorldView-2 bands. Based on the sampling theorem, the 3-m spatial resolution provided an appropriate resolution for all subspecies investigated. The WorldView-2 image was subsequently classified using the partial least squares linear discriminant analysis algorithm and the appropriate spatial resolution. An overall classification accuracy of 90% was established with an allocation disagreement of 9 and a quantity disagreement of 1. Numéro de notice : A2016-458 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2015.1094519 Date de publication en ligne : 26/10/2015 En ligne : http://dx.doi.org/10.1080/10106049.2015.1094519 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81382
in Geocarto international > vol 31 n° 7 - 8 (July - August 2016) . - pp 870 - 880[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2016041 RAB Revue Centre de documentation En réserve L003 Disponible Prediction of categorical spatial data via Bayesian updating / Xiang Huang in International journal of geographical information science IJGIS, vol 30 n° 7- 8 (July - August 2016)
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Titre : Prediction of categorical spatial data via Bayesian updating Type de document : Article/Communication Auteurs : Xiang Huang, Auteur ; Zhizhong Wang, Auteur ; Jianhua Guo, Auteur Année de publication : 2016 Article en page(s) : pp 1426 - 1449 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] analyse spatiale
[Termes IGN] classification dirigée
[Termes IGN] mise à jour automatique
[Termes IGN] système expertRésumé : (Auteur) This study introduces a transition probability-based Bayesian updating (BU) approach for spatial classification through expert system. Transition probabilities are interpreted as expert opinions for updating the prior marginal probabilities of categorical response variables. The main objective of this paper is to provide a spatial categorical variable prediction method which has a solid theoretical foundation and yields relatively higher classification accuracy compared with conventional ones. The basic idea is to first build a linear Bayesian updating (LBU) model that corresponds to an application of Bayes’ theorem. Since the linear opinion pool is intrinsically suboptimal and underconfident, the beta-transformed Bayesian updating (BBU) model is proposed to overcome this limitation. Another type of BU approach, conditional independent Bayesian updating (CIBU), is derived based on conditional independent experts. It is shown that traditional Markovian-type categorical prediction (MCP) is equivalent to a particular CIBU model with specific parameters. As three variants of the BU method, these techniques are illustrated in synthetic and real-world case studies, comparison results with both the LBU and MCP favor the BBU model. Numéro de notice : A2016-310 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2015.1133819 En ligne : http://dx.doi.org/10.1080/13658816.2015.1133819 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80910
in International journal of geographical information science IJGIS > vol 30 n° 7- 8 (July - August 2016) . - pp 1426 - 1449[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2016042 RAB Revue Centre de documentation En réserve L003 Disponible 079-2016041 RAB Revue Centre de documentation En réserve L003 Disponible An evaluation of unsupervised and supervised learning algorithms for clustering landscape types in the United States / Jochen Wendel in Cartography and Geographic Information Science, Vol 43 n° 3 (June 2016)
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Titre : An evaluation of unsupervised and supervised learning algorithms for clustering landscape types in the United States Type de document : Article/Communication Auteurs : Jochen Wendel, Auteur ; Barbara P. Buttenfield, Auteur ; Lauwrence V. Stanislawski, Auteur Année de publication : 2016 Article en page(s) : pp 233 - 249 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] classification dirigée
[Termes IGN] classification non dirigée
[Termes IGN] données hydrographiques
[Termes IGN] Etats-Unis
[Termes IGN] généralisation cartographique automatisée
[Termes IGN] intégration de données
[Termes IGN] système d'information géographiqueRésumé : (Auteur) Knowledge of landscape type can inform cartographic generalization of hydrographic features, because landscape characteristics provide an important geographic context that affects variation in channel geometry, flow pattern, and network configuration. Landscape types are characterized by expansive spatial gradients, lacking abrupt changes between adjacent classes; and as having a limited number of outliers that might confound classification. The US Geological Survey (USGS) is exploring methods to automate generalization of features in the National Hydrography Data set (NHD), to associate specific sequences of processing operations and parameters with specific landscape characteristics, thus obviating manual selection of a unique processing strategy for every NHD watershed unit. A chronology of methods to delineate physiographic regions for the United States is described, including a recent maximum likelihood classification based on seven input variables. This research compares unsupervised and supervised algorithms applied to these seven input variables, to evaluate and possibly refine the recent classification. Evaluation metrics for unsupervised methods include the Davies–Bouldin index, the Silhouette index, and the Dunn index as well as quantization and topographic error metrics. Cross validation and misclassification rate analysis are used to evaluate supervised classification methods. The paper reports the comparative analysis and its impact on the selection of landscape regions. The compared solutions show problems in areas of high landscape diversity. There is some indication that additional input variables, additional classes, or more sophisticated methods can refine the existing classification. Numéro de notice : A2016-166 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/15230406.2015.1067829 En ligne : https://doi.org/10.1080/15230406.2015.1067829 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80472
in Cartography and Geographic Information Science > Vol 43 n° 3 (June 2016) . - pp 233 - 249[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 032-2016031 RAB Revue Centre de documentation En réserve L003 Disponible Context-dependent detection of non-linearly distributed points for vegetation classification in airborne LiDAR / Denis Horvat in ISPRS Journal of photogrammetry and remote sensing, vol 116 (June 2016)
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Titre : Context-dependent detection of non-linearly distributed points for vegetation classification in airborne LiDAR Type de document : Article/Communication Auteurs : Denis Horvat, Auteur ; Borut Žalik, Auteur ; Domen Mongus, Auteur Année de publication : 2016 Article en page(s) : pp 1 – 14 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse de sensibilité
[Termes IGN] classification dirigée
[Termes IGN] détection automatique
[Termes IGN] distribution spatiale
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] méthode robuste
[Termes IGN] morphologie mathématique
[Termes IGN] prise en compte du contexte
[Termes IGN] végétation
[Termes IGN] zone ruraleRésumé : (auteur) This paper proposes a new method for the detection of vegetation in LiDAR data. As vegetation points are characterised by non-linear distributions, they are efficiently recognised based-on large errors obtained when applying the local fitting of planar surfaces. In addition, three contextual filters are introduced capable of dealing with those exceptions that do not conform with previous interpretations. Namely, they are designed for detecting overgrowing vegetation, small objects attached to the planar surfaces (such as balconies, chimneys, and noise within the buildings) and small objects that do not belong to vegetation (vehicles, statues, fences). During the validation, the proposed method achieved over 97% correctness as well as completeness of vegetation recognition in rural areas while its average accuracy in urban settings was 90.7% in terms of F1F1-scores. The method uses only three input parameters and allows for efficient compensation between completeness and correctness, without significantly affecting the F1F1-score. Sensitivity analysis of the method also confirmed the robustness against a sub-optimal definition of the input parameters. Numéro de notice : A2016-576 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.02.011 En ligne : https://doi.org/10.1016/j.isprsjprs.2016.02.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81706
in ISPRS Journal of photogrammetry and remote sensing > vol 116 (June 2016) . - pp 1 – 14[article]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)
[article]
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 routierRésumé : (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
in Cartography and Geographic Information Science > Vol 43 n° 3 (June 2016) . - pp 266 - 282[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 032-2016031 RAB Revue Centre de documentation En réserve L003 Disponible A spectral–structural bag-of-features scene classifier for very high spatial resolution remote sensing imagery / Bei Zhao in ISPRS Journal of photogrammetry and remote sensing, vol 116 (June 2016)PermalinkSupervised classification of very high resolution optical images using wavelet-based textural features / Olivier Regniers in IEEE Transactions on geoscience and remote sensing, vol 54 n° 6 (June 2016)PermalinkActive-metric learning for classification of remotely sensed hyperspectral images / Edoardo Pasolli in IEEE Transactions on geoscience and remote sensing, vol 54 n° 4 (April 2016)PermalinkStreet-side vehicle detection, classification and change detection using mobile laser scanning data / Wen Xiao in ISPRS Journal of photogrammetry and remote sensing, vol 114 (April 2016)PermalinkClassified and clustered data constellation: An efficient approach of 3D urban data management / Suhaibah Azri in ISPRS Journal of photogrammetry and remote sensing, vol 113 (March 2016)PermalinkData fusion technique using wavelet transform and Taguchi methods for automatic landslide detection from airborne laser scanning data and QuickBird satellite imagery / Biswajeet Pradhan in IEEE Transactions on geoscience and remote sensing, vol 54 n° 3 (March 2016)PermalinkMulti-criteria, graph-based road centerline vectorization using ordered weighted averaging operators / Fateme Ameri in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 2 (February 2016)PermalinkA joint Gaussian process model for active visual recognition with expertise estimation in crowdsourcing / Chengjiang Long in International journal of computer vision, vol 116 n° 2 (15th January 2016)PermalinkForest stand segmentation using airborne lidar data and very high resolution multispectral imagery / Clément Dechesne (2016)PermalinkPermalink