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A comparative user study of visualization techniques for cluster analysis of multidimensional data sets / Elio Ventocilla in Information visualization, vol 19 n° 4 (October 2020)
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Titre : A comparative user study of visualization techniques for cluster analysis of multidimensional data sets Type de document : Article/Communication Auteurs : Elio Ventocilla, Auteur ; Maria Riveiro, Auteur Année de publication : 2020 Article en page(s) : pp 318 - 338 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse comparative
[Termes IGN] analyse de groupement
[Termes IGN] données multidimensionnelles
[Termes IGN] modèle logique de données
[Termes IGN] projection
[Termes IGN] utilisateur
[Termes IGN] visualisation de données
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) This article presents an empirical user study that compares eight multidimensional projection techniques for supporting the estimation of the number of clusters, k, embedded in six multidimensional data sets. The selection of the techniques was based on their intended design, or use, for visually encoding data structures, that is, neighborhood relations between data points or groups of data points in a data set. Concretely, we study: the difference between the estimates of k as given by participants when using different multidimensional projections; the accuracy of user estimations with respect to the number of labels in the data sets; the perceived usability of each multidimensional projection; whether user estimates disagree with k values given by a set of cluster quality measures; and whether there is a difference between experienced and novice users in terms of estimates and perceived usability. The results show that: dendrograms (from Ward’s hierarchical clustering) are likely to lead to estimates of k that are different from those given with other multidimensional projections, while Star Coordinates and Radial Visualizations are likely to lead to similar estimates; t-Stochastic Neighbor Embedding is likely to lead to estimates which are closer to the number of labels in a data set; cluster quality measures are likely to produce estimates which are different from those given by users using Ward and t-Stochastic Neighbor Embedding; U-Matrices and reachability plots will likely have a low perceived usability; and there is no statistically significant difference between the answers of experienced and novice users. Moreover, as data dimensionality increases, cluster quality measures are likely to produce estimates which are different from those perceived by users using any of the assessed multidimensional projections. It is also apparent that the inherent complexity of a data set, as well as the capability of each visual technique to disclose such complexity, has an influence on the perceived usability. Numéro de notice : A2020-846 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1177%2F1473871620922166 Date de publication en ligne : 04/07/2020 En ligne : https://doi.org/10.1177%2F1473871620922166 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98650
in Information visualization > vol 19 n° 4 (October 2020) . - pp 318 - 338[article]Comparing features of single and multi-photon lidar in boreal forests / Xiaowei Yu in ISPRS Journal of photogrammetry and remote sensing, vol 168 (October 2020)
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Titre : Comparing features of single and multi-photon lidar in boreal forests Type de document : Article/Communication Auteurs : Xiaowei Yu, Auteur ; Antero Kukko, Auteur ; Harri Kaartinen, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 268 - 276 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse comparative
[Termes IGN] canopée
[Termes IGN] diamètre à hauteur de poitrine
[Termes IGN] données lidar
[Termes IGN] Finlande
[Termes IGN] forêt boréale
[Termes IGN] modèle numérique de surface
[Termes IGN] photon
[Termes IGN] semis de points
[Termes IGN] télémétrie laser aéroportéRésumé : (auteur) The emerging single-photon laser scanning has made technological breakthrough in the collection of airborne laser scanning data. In principle, single-photon systems require only one detected photon for successful ranging. Further, the point density on the ground can be 10–100 times higher for single-photon lidar data than that obtained with multi-photon systems at the same flight altitude. This has great potential to reduce operation costs. Single-photon lidar technology is assumed to be the best for data acquisition when high point densities are required over very large areas, or when improvements in measurement rates can significantly reduce data acquisition costs, such as in nationwide laser scanning programmes, where the whole country is repeatedly covered with data every 5–10 years. This study investigates single-photon lidar and conventional multi-photon laser scanning data for their potential in characterizing ground and forest attributes. Performance is evaluated in a boreal forest by a comparative analysis, where single-photon lidar measurements with SPL100 (Leica/Hexagon) from two flight heights (1900 m and 3800 m) are compared with data from the Optech Titan (400 m) multi-photon airborne laser scanning (ALS) under summer conditions (i.e. leaves on). We found that SPL100 from both altitudes provides forest attribute estimates with comparable accuracy to that of Optech Titan from 400 m using an area-based method. This demonstrates that point density and flight altitude do not have significant impact on forest attribute estimation using the area-based approach. As a result, SPL100 is a cost-efficient alternative to a conventional laser scanner for forest inventories at large scale. There are systematic differences in behavior of the data sets due to differences in ranging sensitivity, beam size, and point density. We observed a higher proportion of ground returns in the SPL100 (3800 m) than in SPL100 (1900 m) data. Both SPL100 data in general produced a higher proportion of ground returns than Titan single channel did in structurally more homogeneous and one layer stands while higher proportion of ground returns from Titan than from SPL100 data in multi-layer stands. Forest structure and flight altitude has a notable impact on the distribution of points and further characteristics of the vertical structures. The pulse of Titan sensor penetrated deeper into the canopy than SPL100. Numéro de notice : A2020-637 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.08.013 Date de publication en ligne : 01/09/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.08.013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96060
in ISPRS Journal of photogrammetry and remote sensing > vol 168 (October 2020) . - pp 268 - 276[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2020101 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020103 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020102 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt A LiDAR aiding ambiguity resolution method using fuzzy one-to-many feature matching / Chuang Qian in Journal of geodesy, vol 94 n° 10 (October 2020)
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Titre : A LiDAR aiding ambiguity resolution method using fuzzy one-to-many feature matching Type de document : Article/Communication Auteurs : Chuang Qian, Auteur ; Hongjuan Zhang, Auteur ; Wenzhuo Li, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 18 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement
[Termes IGN] analyse comparative
[Termes IGN] corrélation à l'aide de traits caractéristiques
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] estimation de position
[Termes IGN] GPS assisté pour la navigation (technologies)
[Termes IGN] logique floue
[Termes IGN] positionnement cinématique en temps réel
[Termes IGN] précision du positionnement
[Termes IGN] résolution d'ambiguïtéRésumé : (auteur) Despite the high-precision performance of GNSS real-time kinematic (RTK) in many cases, large noises in pseudo-range measurements or harsh signal environments still impact float ambiguity estimation in kinematic localization, which leads to ambiguity-fixed failure and worse positioning results. To improve RTK ambiguity resolution (AR) performance further, multi-sensor fusion technique is a feasible option. Light detection and ranging (LiDAR)-based localization is a good complementary method to GNSS. Tight integration of GNSS RTK and LiDAR adds new information to satellite measurements, thus improving float ambiguity estimation and then improving integer AR. In this work, a LiDAR aiding single-frequency single-epoch GPS + BDS RTK was proposed and investigated by theoretical analysis and performance assessment. Considering LiDAR-based localization failure because of ambiguous and repetitive landmarks, a fuzzy one-to-many feature-matching method was proposed to find a series of sequences including all possible relative positions to landmarks. Then, the standard RTK method was tightly combined with the possible positions from each sequence to find the most accurate position estimation. Experimental results proved the superiority of our method over the standard RTK method in all aspects of success rate, fixed rate and positioning accuracy. In specific, our method achieved centimeter-level position accuracy with 100% fixed rate in the urban environment, while the standard GPS + BDS RTK obtained decimeter-level accuracy with 26.84% fixed rate. In the high occlusion environment, our method had centimeter-level accuracy with a fixed rate of 96.31%, comparing a meter-level accuracy and a fixed rate of 7.65% of standard GPS + BDS RTK method. Numéro de notice : A2020-649 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-020-01426-z Date de publication en ligne : 25/11/2020 En ligne : https://doi.org/10.1007/s00190-020-01426-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96082
in Journal of geodesy > vol 94 n° 10 (October 2020) . - 18 p.[article]Multiview automatic target recognition for infrared imagery using collaborative sparse priors / Xuelu Li in IEEE Transactions on geoscience and remote sensing, vol 58 n° 10 (October 2020)
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Titre : Multiview automatic target recognition for infrared imagery using collaborative sparse priors Type de document : Article/Communication Auteurs : Xuelu Li, Auteur ; Vishal Monga, Auteur ; Abhijit Mahalanobis, Auteur Année de publication : 2020 Article en page(s) : pp 6776 - 6790 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] ajustement de paramètres
[Termes IGN] apprentissage profond
[Termes IGN] détection de cible
[Termes IGN] données clairsemées
[Termes IGN] estimation bayesienne
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image à basse résolution
[Termes IGN] image infrarouge
[Termes IGN] reconnaissance automatiqueRésumé : (auteur) The low resolution of infrared (IR) images makes feature extraction for classification of a challenging work. Learning-based methods, therefore, are preferred to be used on such raw imagery. In this article, in order to avoid difficulties in feature extraction, a novel multitask extension of the widely used sparse-representation-classification (SRC) method is proposed in both single and multiview set-ups. That is, the test sample could be a single IR image or images from different views. In both single-view and multiview scenarios, we try to employ collaborative spike and slab priors. This is because the traditional sparsity-inducing measures such as the l0 -row pseudonorm makes it hard to capture the sparse structure of the coefficient matrix when expanded in terms of a training dictionary, and the priors are proved to be able to capture fairly general sparse structures. Furthermore, a joint prior and sparse coefficient estimation method (JPCEM) is proposed for the first time in this article in order to alleviate the need to handpick prior parameters required before classification. Multiple experiments are conducted on a synthetic Comanche Forward Looking IR (FLIR) Automatic Target Recognition (ATR) database collected by Army Research Lab and a challenging mid-wave IR (MWIR) image ATR database made available by the U.S. Army Night Vision and Electronic Sensors Directorate. The final results substantiate the merits of the proposed JPCEM through comparisons with other state-of-the-art methods, including both the ones based on SRC and the ones constructed using deep learning frameworks. Numéro de notice : A2020-584 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2973969 Date de publication en ligne : 26/03/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2973969 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95908
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 10 (October 2020) . - pp 6776 - 6790[article]OpenStreetMap quality assessment using unsupervised machine learning methods / Kent T. Jacobs in Transactions in GIS, Vol 24 n° 5 (October 2020)
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Titre : OpenStreetMap quality assessment using unsupervised machine learning methods Type de document : Article/Communication Auteurs : Kent T. Jacobs, Auteur ; Scott W. Mitchell, Auteur Année de publication : 2020 Article en page(s) : pp 1280-1298 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] analyse comparative
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage non-dirigé
[Termes IGN] approche participative
[Termes IGN] Canada
[Termes IGN] données localisées des bénévoles
[Termes IGN] estimation de précision
[Termes IGN] fiabilité des données
[Termes IGN] OpenStreetMap
[Termes IGN] Ottawa
[Termes IGN] qualité des donnéesRésumé : (Auteur) The reliability and quality of volunteered geographic information (VGI) continue to be pressing concerns. Many VGI projects lack standard geospatial data quality assurance procedures, and the reliability of contributors remains in question. Traditional approaches rely on comparing VGI to an “authoritative” or “gold standard” dataset to assess quality. This study investigates VGI quality by analysing the OpenStreetMap (OSM) database in Ottawa‐Gatineau, focusing on historical map features and contributor data to gain an understanding of how users are contributing to the database, and their ability to do so accurately. Unsupervised machine learning analyses expose a cluster of experienced contributors classified as “OSM validators/experts”, which are then further used to attribute data quality. They are identified through a combination of strong contribution loadings associated with the use and experience of advanced OSM editors, and weaker loadings associated with feature creation and frequency of contributions leading to further correction. Limitations are discussed with implications for future work. Numéro de notice : A2020-701 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12680 Date de publication en ligne : 18/08/2020 En ligne : https://doi.org/10.1111/tgis.12680 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96224
in Transactions in GIS > Vol 24 n° 5 (October 2020) . - pp 1280-1298[article]See the forest and the trees: Effective machine and deep learning algorithms for wood filtering and tree species classification from terrestrial laser scanning / Zhouxin Xi in ISPRS Journal of photogrammetry and remote sensing, vol 168 (October 2020)
PermalinkTree species classification using structural features derived from terrestrial laser scanning / Louise Terryn in ISPRS Journal of photogrammetry and remote sensing, vol 168 (October 2020)
PermalinkUse of visible and near-infrared reflectance spectroscopy models to determine soil erodibility factor (K) in an ecologically restored watershed / Qinghu Jiang in Remote sensing, vol 12 n° 18 (September-2 2020)
PermalinkAncient forest statistics provide centennial perspective over the status and dynamics of forest area in France / Timothée Audinot in Annals of Forest Science, vol 77 n° 3 (September 2020)
PermalinkAssessment of landslide susceptibility at a local spatial scale applying the multi-criteria analysis and GIS: a case study from Slovakia / Jana Vojteková in Geomatics, Natural Hazards and Risk, vol 11 n° 1 (2020)
PermalinkComparing pedestrians’ gaze behavior in desktop and in real environments / Weihua Dong in Cartography and Geographic Information Science, Vol 47 n° 5 (September 2020)
PermalinkComparison of tree-based classification algorithms in mapping burned forest areas / Dilek Kucuk Matci in Geodetski vestnik, vol 64 n° 3 (September - November 2020)
PermalinkComparison of two methods for multiresolution terrain modelling in GIS / Turkay Gokgoz in Geocarto international, vol 35 n° 12 ([01/09/2020])
PermalinkComprehensive decision-strategy space exploration for efficient territorial planning strategies / Olivier Billaud in Computers, Environment and Urban Systems, vol 83 (September 2020)
PermalinkGeo-environment risk assessment in Zhengzhou City, China / Chuanming Ma in Geomatics, Natural Hazards and Risk, vol 11 n° 1 (2020)
PermalinkHeliport detection using artificial neural networks / Emre Baseski in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 9 (September 2020)
PermalinkPrecise extraction of citrus fruit trees from a Digital Surface Model using a unified strategy: detection, delineation, and clustering / Ali Ozgun Ok in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 9 (September 2020)
PermalinkComparative study of different models for soil erosion and sediment yield in Pairi watershed, Chhattisgarh, India / Tarun Kumar in Geocarto international, vol 35 n° 11 ([01/08/2020])
PermalinkGuided feature matching for multi-epoch historical image blocks pose estimation / Lulin Zhang in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2020 (August 2020)
PermalinkReintroduction of the European bison (Bison bonasus) in central-eastern Europe: a case study / Cathlin M. Lord in International journal of geographical information science IJGIS, vol 34 n° 8 (August 2020)
PermalinkSize dependency of variables influencing fire occurrence in Mediterranean forests of Eastern Spain / Marina Peris-Llopis in European Journal of Forest Research, vol 139 n°4 (August 2020)
PermalinkEvaluations of the significant wave height products of HY-2B satellite radar altimeters / Yongjun Jia in Marine geodesy, Vol 43 n° 4 (July 2020)
PermalinkGIS-based MCDM – AHP modeling for flood susceptibility mapping of arid areas, southeastern Tunisia / Dhekra Souissi in Geocarto international, vol 35 n° 9 ([01/07/2020])
PermalinkMap construction algorithms: a local evaluation through hiking data / David Duran in Geoinformatica, vol 24 n° 3 (July 2020)
PermalinkMoGUS, un outil de modélisation et d'analyse comparative des trames urbaines / Dominique Badariotti in Revue internationale de géomatique, vol 30 n° 3-4 (juillet - décembre 2020)
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