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Unsupervised classification of multispectral images embedded with a segmentation of panchromatic images using localized clusters / Ting Mao in IEEE Transactions on geoscience and remote sensing, vol 57 n° 11 (November 2019)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 57 n° 11 (November 2019) . - pp 8732 - 8744 Titre : Unsupervised classification of multispectral images embedded with a segmentation of panchromatic images using localized clusters Type de document : Article/Communication Auteurs : Ting Mao, Auteur ; Wei Huang, Auteur Année de publication : 2019 Article en page(s) : pp 8732 - 8744 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] algorithme de fusion [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] Chine [Termes descripteurs IGN] classification non dirigée [Termes descripteurs IGN] fusion d'images [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image panchromatique [Termes descripteurs IGN] précision de la classification [Termes descripteurs IGN] segmentation d'image [Termes descripteurs IGN] segmentation multirésolution [Termes descripteurs IGN] superpixel Résumé : (auteur) There are many approaches to fuse panchromatic (PAN) and multispectral (MS) images for classification, mainly including sharpening-then-classification methods, classification-then-sharpening methods, and segmentation-then-classification methods. The generalized Chinese restaurant franchise (gCRF) is a segmentation-then-classification-like method to fuse very high resolution (VHR) PAN and MS images for classification, which has the limitation the same as that of the general segmentation-then-classification methods that segmentation errors will affect the subsequent classification. The problems of gCRF are that during the segmentation step, the spatial coherence in the image plane is deficient and the global clusters without spatial position information are used for segmentation, which may lead to undersegmented and disconnected regions in the segmentation results and decrease classification accuracy. In this paper, we propose an improved model, which overcomes the problems of the gCRF during the segmentation step, to increase the classification accuracy by the following two ways: 1) building the spatial coherence in the image plane by introducing neighborhood information of superpixels to construct the subimages and 2) using localized clusters with spatial location information instead of global clusters to measure the similarity between superpixels and segments. The experimental results show that the problems of undersegmentation and disconnected segments are both alleviated, resulting in better classification results in terms of the visual and quantitative aspects. Numéro de notice : A2019-597 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2922672 date de publication en ligne : 17/07/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2922672 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94589 [article]

A reliable traffic prediction approach for bike‐sharing system by exploiting rich information with temporal link prediction strategy / Yan Zhou in Transactions in GIS, Vol 23 n° 5 (October 2019)  

Public [article]  inTransactions in GIS > Vol 23 n° 5 (October 2019) . - pp 1125 - 1151 Titre : A reliable traffic prediction approach for bike‐sharing system by exploiting rich information with temporal link prediction strategy Type de document : Article/Communication Auteurs : Yan Zhou, Auteur ; Yanxi Li, Auteur ; Qing Zhu, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 1125 - 1151 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] analyse spatio-temporelle [Termes descripteurs IGN] bicyclette [Termes descripteurs IGN] données météorologiques [Termes descripteurs IGN] gestion prévisionnelle [Termes descripteurs IGN] New York (Etats-Unis ; ville) [Termes descripteurs IGN] regroupement de données [Termes descripteurs IGN] SOLAP [Termes descripteurs IGN] trafic routier [Termes descripteurs IGN] Washington D.C. Résumé : (auteur) Bike‐sharing systems have been widely used in major cities across the world. As bike borrowing and return at different stations in different periods are not balanced, the bikes in a bike‐sharing system need to be redistributed frequently to rebalance the system. Therefore, traffic flow forecasting of the bike‐sharing system is an important issue, as this is conducive to achieving rebalancing of the bike system. In this article, we present a new traffic flow prediction approach based on the temporal links in dynamic traffic flow networks. A station clustering algorithm is first introduced to cluster stations into groups. A temporal link prediction method based on the dynamic traffic flow network method (STW+M) is then proposed to predict the traffic flow between stations. In our method, the non‐negative tensor decomposition and time‐series analysis model capture the rich information (temporal variabilities, spatial characteristics, and weather information) of the across‐clusters transition. Then, a temporal link prediction strategy is used to forecast potential links and weights in the traffic flow network by investigating both the network structure and the results of tensor computations. In order to assess the methods proposed in this article, we have used the data of bike‐sharing systems in New York and Washington, DC to conduct bike traffic prediction and the experimental results have shown that our method produces the lowest root mean square error (RMSE) and mean square error (MSE). Compared to four prediction methods from the literature, our RMSE and MSE of the two datasets have been lowered by an average of 2.55 (Washington, DC) and 2.41 (New York) and 3.35 (Washington, DC) and 2.96 (New York), respectively. The results show that the proposed approach improves predictions of traffic flow. Numéro de notice : A2019-551 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12560 date de publication en ligne : 03/07/2019 En ligne : https://doi.org/10.1111/tgis.12560 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94201 [article]

Spatially constrained regionalization with multilayer perceptron / Michael Govorov in Transactions in GIS, Vol 23 n° 5 (October 2019)  

Public [article]  inTransactions in GIS > Vol 23 n° 5 (October 2019) . - pp 1048 - 1077 Titre : Spatially constrained regionalization with multilayer perceptron Type de document : Article/Communication Auteurs : Michael Govorov, Auteur ; Giedre Beconyte, Auteur ; Gennady Gienko, Auteur ; Victor Putrenko, Auteur Année de publication : 2019 Article en page(s) : pp 1048 - 1077 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] classification dirigée [Termes descripteurs IGN] données géologiques [Termes descripteurs IGN] Perceptron multicouche [Termes descripteurs IGN] programmation par contraintes [Termes descripteurs IGN] régionalisation [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] segmentation par graphes d'adjacence de régions [Termes descripteurs IGN] Ukraine [Termes descripteurs IGN] uranium Résumé : (auteur) In this article, multilayer perceptron (MLP) network models with spatial constraints are proposed for regionalization of geostatistical point data based on multivariate homogeneity measures. The study focuses on non stationarity and autocorrelation in spatial data. Supervised MLP machine learning algorithms with spatial constraints have been implemented and tested on a point dataset. MLP spatially weighted classification models and an MLP contiguity constrained classification model are developed to conduct spatially constrained regionalization. The proposed methods have been tested with an attribute‐rich point dataset of geological surveys in Ukraine. The experiments show that consideration of the spatial effects, such as the use of spatial attributes and their respective whitening, improve the output of regionalization. It is also shown that spatial sorting used to preserve spatial contiguity leads to improved regionalization performance. Numéro de notice : A2019-552 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12557 date de publication en ligne : 09/07/2019 En ligne : https://doi.org/10.1111/tgis.12557 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94202 [article]

Piecewise-planar approximation of large 3D data as graph-structured optimization / Stéphane Guinard in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-2/W5 (may 2019)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol IV-2/W5 (may 2019) . - pp 365 - 372 Titre : Piecewise-planar approximation of large 3D data as graph-structured optimization Type de document : Article/Communication Auteurs : Stéphane Guinard , Auteur ; Loïc Landrieu , Auteur ; Laurent Caraffa , Auteur ; Bruno Vallet , Auteur Congrès : ISPRS Geospatial Week 2019 (10 - 14 juin 2019; Enschede, Pays-Bas), Commanditaire Année de publication : 2019 Article en page(s) : pp 365 - 372 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] algorithme de décalage moyen [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] approximation [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] érosion anthropique [Termes descripteurs IGN] erreur d'approximation [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] graphe [Termes descripteurs IGN] maillage [Termes descripteurs IGN] Ransac (algorithme) [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] surface plane Résumé : (auteur) We introduce a new method for the piecewise-planar approximation of 3D data, including point clouds and meshes. Our method is designed to operate on large datasets (e.g. millions of vertices) containing planar structures, which are very frequent in anthropic scenes. Our approach is also adaptive to the local geometric complexity of the input data. Our main contribution is the formulation of the piecewise-planar approximation problem as a non-convex optimization problem. In turn, this problem can be efficiently solved with a graph-structured working set approach. We compare our results with a state-of-the-art region-growing-based segmentation method and show a significant improvement both in terms of approximation error and computation efficiency. Numéro de notice : A2019-592 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-IV-2-W5-365-2019 date de publication en ligne : 29/05/2019 En ligne : https://doi.org/10.5194/isprs-annals-IV-2-W5-365-2019 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94552 [article]

Exploring the uncertainty of activity zone detection using digital footprints with multi-scaled DBSCAN / Xinyi Liu in International journal of geographical information science IJGIS, Vol 33 n° 5-6 (May - June 2019)  

Public [article]  inInternational journal of geographical information science IJGIS > Vol 33 n° 5-6 (May - June 2019) . - pp 1196 - 1223 Titre : Exploring the uncertainty of activity zone detection using digital footprints with multi-scaled DBSCAN Type de document : Article/Communication Auteurs : Xinyi Liu, Auteur ; Qunying Huang, Auteur ; Song Gao, Auteur Année de publication : 2019 Article en page(s) : pp 1196 - 1223 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] données issues des réseaux sociaux [Termes descripteurs IGN] mobilité urbaine [Termes descripteurs IGN] réseau social [Termes descripteurs IGN] Twitter Mots-clés libres : density-based spatial clustering of applications with noise (DBSCAN) Résumé : (Auteur) The density-based spatial clustering of applications with noise (DBSCAN) method is often used to identify individual activity clusters (i.e., zones) using digital footprints captured from social networks. However, DBSCAN is sensitive to the two parameters, eps and minpts. This paper introduces an improved density-based clustering algorithm, Multi-Scaled DBSCAN (M-DBSCAN), to mitigate the detection uncertainty of clusters produced by DBSCAN at different scales of density and cluster size. M-DBSCAN iteratively calibrates suitable local eps and minpts values instead of using one global parameter setting as DBSCAN for detecting clusters of varying densities, and proves to be effective for detecting potential activity zones. Besides, M-DBSCAN can significantly reduce the noise ratio by identifying all points capturing the activities performed in each zone. Using the historic geo-tagged tweets of users in Washington, D.C. and in Madison, Wisconsin, the results reveal that: 1) M-DBSCAN can capture dispersed clusters with low density of points, and therefore detecting more activity zones for each user; 2) A value of 40 m or higher should be used for eps to reduce the possibility of collapsing distinctive activity zones; and 3) A value between 200 and 300 m is recommended for eps while using DBSCAN for detecting activity zones. Numéro de notice : A2019-445 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1563301 date de publication en ligne : 09/01/2019 En ligne : https://doi.org/10.1080/13658816.2018.1563301 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92781 [article]

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