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A GIS-based comparative study of Dempster-Shafer, logistic regression and artificial neural network models for landslide susceptibility mapping / Wei Chen in Geocarto international, vol 32 n° 4 (April 2017)
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Titre : A GIS-based comparative study of Dempster-Shafer, logistic regression and artificial neural network models for landslide susceptibility mapping Type de document : Article/Communication Auteurs : Wei Chen, Auteur ; Hamid Reza Pourghasemi, Auteur ; Zhou Zhao, Auteur Année de publication : 2017 Article en page(s) : pp 367 - 385 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] aléa
[Termes IGN] analyse comparative
[Termes IGN] ArcGIS
[Termes IGN] cartographie des risques
[Termes IGN] Chine
[Termes IGN] classification de Dempster-Shafer
[Termes IGN] classification par réseau neuronal
[Termes IGN] effondrement de terrain
[Termes IGN] régression logistique
[Termes IGN] risque naturel
[Termes IGN] vulnérabilitéRésumé : (Auteur) The main aim of present study is to compare three GIS-based models, namely Dempster–Shafer (DS), logistic regression (LR) and artificial neural network (ANN) models for landslide susceptibility mapping in the Shangzhou District of Shangluo City, Shaanxi Province, China. At First, landslide locations were identified by aerial photographs and supported by field surveys, and a total of 145 landslide locations were mapped in the study area. Subsequently, the landslide inventory was randomly divided into two parts (70/30) using Hawths Tools in ArcGIS 10.0 for training and validation purposes, respectively. In the present study, 14 landslide conditioning factors such as altitude, slope angle, slope aspect, topographic wetness index, sediment transport index, stream power index, plan curvature, profile curvature, lithology, rainfall, distance to rivers, distance to roads, distance to faults and normalized different vegetation index were used to detect the most susceptible areas. In the next step, landslide susceptible areas were mapped using the DS, LR and ANN models based on landslide conditioning factors. Finally, the accuracies of the landslide susceptibility maps produced from the three models were verified using the area under the curve (AUC). The validation results showed that the landslide susceptibility map generated by the ANN model has the highest training accuracy (73.19%), followed by the LR model (71.37%), and the DS model (66.42%). Similarly, the AUC plot for prediction accuracy presents that ANN model has the highest accuracy (69.62%), followed by the LR model (68.94%), and the DS model (61.39%). According to the validation results of the AUC curves, the map produced by these models exhibits the satisfactory properties. Numéro de notice : A2017-271 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1140824 Date de publication en ligne : 22/03/2016 En ligne : http://doi.org/10.1080/10106049.2016.1140824 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85297
in Geocarto international > vol 32 n° 4 (April 2017) . - pp 367 - 385[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2017041 RAB Revue Centre de documentation En réserve L003 Disponible Multilayer NMF for blind unmixing of hyperspectral imagery with additional constraints / L. Chen in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 4 (April 2017)
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Titre : Multilayer NMF for blind unmixing of hyperspectral imagery with additional constraints Type de document : Article/Communication Auteurs : L. Chen, Auteur ; Shengbo Chen, Auteur ; Xulin Guo, Auteur Année de publication : 2017 Article en page(s) : pp 307 - 316 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] calcul matriciel
[Termes IGN] contrainte spectrale
[Termes IGN] factorisation de matrice non-négative
[Termes IGN] filtrage du bruit
[Termes IGN] image hyperspectrale
[Termes IGN] processus de hiérarchisation analytique
[Termes IGN] programmation par contraintes
[Termes IGN] réflectanceRésumé : (Auteur) Due to the coincidence of hyperspectral reflectance nonnegativity (and its corresponding abundance) with nonnegative matrix factorization (NMF) methods, NMF has been widely applied to unmix hyperspectral images in recent years. However, many local minima persist because of the nonconvexity of the objective function. Thus, the nonnegativity constraint is not sufficient and additional auxiliary constraints should be applied to objective functions. In this paper, a new approach we call constrained multilayer NMF (CMLNMF), is proposed for hyperspectral data. In this approach, the mixed spectra are regarded as endmember signatures that has been contaminated by multiplicative noise. The purpose of CMLNMF is to eliminate noise by hierarchical processing until the endmember spectra are obtained. Also, the hierarchical processing is self-adaptive to make the algorithm more effective. Furthermore, in each layer two constraints are implemented on the objective function. One is sparseness on the abundance matrix and the other is minimum volume on the spectral matrix. The hierarchical processing separates the abundance matrix into a series of matrices that make the characteristic of sparseness more obvious and meaningful. The proposed algorithm is applied to synthetic data and real hyperspectral data for quantitative evaluation. According to the comparison with other algorithms, CMLNMF has better performance and provides effective solutions for blind unmixing of hyperspectral image data. Numéro de notice : A2017-112 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE/MATHEMATIQUE Nature : Article DOI : 10.14358/PERS.83.4.307 En ligne : https://doi.org/10.14358/PERS.83.4.307 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84590
in Photogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 4 (April 2017) . - pp 307 - 316[article]Panda∗: A generic and scalable framework for predictive spatio-temporal queries / Abdeltawab M. Hendawi in Geoinformatica, vol 21 n° 2 (April - June 2017)
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Titre : Panda∗: A generic and scalable framework for predictive spatio-temporal queries Type de document : Article/Communication Auteurs : Abdeltawab M. Hendawi, Auteur ; Mohamed Ali, Auteur ; Mohamed F. Mokbel, Auteur Année de publication : 2017 Article en page(s) : pp 175 - 208 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] environnement de développement
[Termes IGN] espace euclidien
[Termes IGN] gestion de trafic
[Termes IGN] objet mobile
[Termes IGN] plus proche voisin, algorithme du
[Termes IGN] prédiction
[Termes IGN] requête spatiotemporelleRésumé : (Auteur) Predictive spatio-temporal queries are crucial in many applications. Traffic management is an example application, where predictive spatial queries are issued to anticipate jammed areas in advance. Also, location-aware advertising is another example application that targets customers expected to be in the vicinity of a shopping mall in the near future. In this paper, we introduce Panda∗, a generic framework for supporting spatial predictive queries over moving objects in Euclidean spaces. Panda∗ distinguishes itself from previous work in spatial predictive query processing by the following features: (1) Panda∗ is generic in terms of supporting commonly-used types of queries, (e.g., predictive range, KNN, aggregate queries) over stationary points of interests as well as moving objects. (2) Panda∗ employees a prediction function that provides accurate prediction even under the absence or the scarcity of the objects’ historical trajectories. (3) Panda∗ is customizable in the sense that it isolates the prediction calculation from query processing. Hence, it enables the injection and integration of user defined prediction functions within its query processing framework. (4) Panda∗ deals with uncertainties and variabilities in the expected travel time from source to destination in response to incomplete information and/or dynamic changes in the underlying Euclidean space. (5) Panda∗ provides a controllable parameter that trades low latency responses for computational resources. Experimental analysis proves the scalability of Panda∗ in evaluating a massive volume of predictive queries over large numbers of moving objects. Numéro de notice : A2017-068 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-016-0284-8 En ligne : http://dx.doi.org/10.1007/s10707-016-0284-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84295
in Geoinformatica > vol 21 n° 2 (April - June 2017) . - pp 175 - 208[article]A spatial anomaly points and regions detection method using multi-constrained graphs and local density / Yan Shi in Transactions in GIS, vol 21 n° 2 (April 2017)
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Titre : A spatial anomaly points and regions detection method using multi-constrained graphs and local density Type de document : Article/Communication Auteurs : Yan Shi, Auteur ; Min Deng, Auteur ; Xuexi Yang, Auteur ; Qiliang Liu, Auteur Année de publication : 2017 Article en page(s) : pp 376 – 405 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] analyse de données
[Termes IGN] analyse spatiale
[Termes IGN] attribut sémantique
[Termes IGN] cartographie statistique
[Termes IGN] détection d'anomalie
[Termes IGN] graphe
[Termes IGN] interpolation spatiale
[Termes IGN] programmation par contraintes
[Termes IGN] triangulation de DelaunayRésumé : (auteur) Spatial anomalies may be single points or small regions whose non-spatial attribute values are significantly inconsistent with those of their spatial neighborhoods. In this article, a Spatial Anomaly Points and Regions Detection method using multi-constrained graphs and local density (SAPRD for short) is proposed. The SAPRD algorithm first models spatial proximity relationships between spatial entities by constructing a Delaunay triangulation, the edges of which provide certain statistical characteristics. By considering the difference in non-spatial attributes of adjacent spatial entities, two levels of non-spatial attribute distance constraints are imposed to improve the proximity graph. This produces a series of sub-graphs, and those with very few entities are identified as candidate spatial anomalies. Moreover, the spatial anomaly degree of each entity is calculated based on the local density. A spatial interpolation surface of the spatial anomaly degree is generated using the inverse distance weight, and this is utilized to reveal potential spatial anomalies and reflect their whole areal distribution. Experiments on both simulated and real-life spatial databases demonstrate the effectiveness and practicability of the SAPRD algorithm. Numéro de notice : A2017-167 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12208 En ligne : http://dx.doi.org/10.1111/tgis.12208 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84701
in Transactions in GIS > vol 21 n° 2 (April 2017) . - pp 376 – 405[article]Unsupervised feature learning for land-use scene recognition / Jiayuan Fan in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)
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Titre : Unsupervised feature learning for land-use scene recognition Type de document : Article/Communication Auteurs : Jiayuan Fan, Auteur ; Tao Chen, Auteur ; Shijian Lu, Auteur Année de publication : 2017 Article en page(s) : pp 2250 - 2261 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] algorithme d'apprentissage
[Termes IGN] analyse discriminante
[Termes IGN] codage
[Termes IGN] image proche infrarouge
[Termes IGN] image RVB
[Termes IGN] invariant
[Termes IGN] pouvoir de résolution géométrique
[Termes IGN] reconnaissance automatique
[Termes IGN] Singapour
[Termes IGN] utilisation du solRésumé : (Auteur) This paper proposes a novel unsupervised feature learning algorithm for land-use scene recognition on very high resolution remote sensing imagery. The proposed technique utilizes a multipath sparse coding architecture in order to capture multiple aspects of discriminative structures within complex remote sensing sceneries. Unlike the previous sparse coding and bag-of-visual-words-based techniques that rely on the handcrafted feature descriptors such as scale-invariant feature transform, the proposed technique extracts dense low-level features from the raw data, including the visual (RGB) data and near-infrared (NIR) data, using image patches of varying sizes at different layers. The proposed technique has been evaluated on three data sets, including the 21-category UC Merced landuse RGB data set with a 1-ft spatial resolution, the 9-category ground scene RGB-NIR data set, and the 10-category Singapore land-use RGB-NIR data set with a 0.5-m spatial resolution. The experimental results show that the proposed technique outperforms the state-of-the-art methods. Numéro de notice : A2107-174 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2640186 En ligne : https://doi.org/10.1109/TGRS.2016.2640186 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84723
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 4 (April 2017) . - pp 2250 - 2261[article]A virtual globe-based visualization and interactive framework for a small craft navigation assistance system in the near sea / Xinzhu Liu in Journal of Traffic and Transportation Engineering (English Edition), n° (April 2017)
PermalinkAn evaluation of a visual analytics prototype for calendar-related spatiotemporal periodicity detection and analysis / Brian Swedberg in Cartographica, vol 52 n° 1 (Spring 2017)
PermalinkConstrained clustering by constraint programming / Thi-Bich-Hanh Dao in Artificial intelligence, vol 244 (March 2017)
PermalinkPermalinkPermalinkStudy of the effects on GPS coordinate time series caused by higher-order ionospheric corrections calculated using the DIPOLE model / Liansheng Deng in Geodesy and Geodynamics, vol 8 n° 2 (March 2017)
PermalinkTowards a knowledge base to support geoprocessing workflow development / Barbara Hofer in International journal of geographical information science IJGIS, vol 31 n° 3-4 (March-April 2017)
PermalinkVisual exploration of movement and event data with interactive time masks / Natalia Andrienko in Visual Informatics, vol 1 n° 1 (March 2017)
PermalinkAdaptive spectral–spatial compression of hyperspectral image with sparse representation / Wei Fu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 2 (February 2017)
PermalinkL’API Istex : le sésame pour accéder aux ressources acquises / Claire Niederlender in Arabesques, n° 84 (février - mars 2017)
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