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Active learning with gaussian process classifier for hyperspectral image classification / Shujing Sun in IEEE Transactions on geoscience and remote sensing, vol 53 n° 4 (April 2015)
[article]
Titre : Active learning with gaussian process classifier for hyperspectral image classification Type de document : Article/Communication Auteurs : Shujing Sun, Auteur ; Ping Zhong, Auteur ; Huaitie Xiao, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 1746 - 1760 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] classification bayesienne
[Termes IGN] image hyperspectraleRésumé : (Auteur) Gaussian process (GP) classifiers represent a powerful and interesting theoretical framework for the Bayesian classification of hyperspectral images. However, the collection of labeled samples is time consuming and costly for hyperspectral data, and the training samples available are often not enough for an adequate learning of the GP classifier. Moreover, the computational cost of performing inference using GP classifiers scales cubically with the size of the training set. To address the limitations of GP classifiers for hyperspectral image classification, reducing the label cost and keeping the training set in a moderate size, this paper introduces an active learning (AL) strategy to collect the most informative training samples for manual labeling. First, we propose three new AL heuristics based on the probabilistic output of GP classifiers aimed at actively selecting the most uncertain and confusing candidate samples from the unlabeled data. Moreover, we develop an incremental model updating scheme to avoid the repeated training of the GP classifiers during the AL process. The proposed approaches are tested on the classification of two realworld hyperspectral data. Comparison with random sampling method reveals a better accuracy gain and faster convergence with the number of queries, and comparison with recent active learning approaches shows a competitive performance. Experimental results also verified the efficiency of the incremental model updating scheme. Numéro de notice : A2015-171 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2347343 Date de publication en ligne : 29/08/2014 En ligne : https://doi.org/10.1109/TGRS.2014.2347343 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75887
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 4 (April 2015) . - pp 1746 - 1760[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015041 RAB Revue Centre de documentation En réserve L003 Disponible Co-clustering geo-referenced time series: exploring spatio-temporal patterns in Dutch temperature data / Xiaojing Wu in International journal of geographical information science IJGIS, vol 29 n° 4 (April 2015)
[article]
Titre : Co-clustering geo-referenced time series: exploring spatio-temporal patterns in Dutch temperature data Type de document : Article/Communication Auteurs : Xiaojing Wu, Auteur ; Raul Zurita-Milla, Auteur ; Menno-Jan Kraak, Auteur Année de publication : 2015 Article en page(s) : pp 624 - 642 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de groupement
[Termes IGN] analyse spatio-temporelle
[Termes IGN] exploration de données géographiques
[Termes IGN] regroupement de données
[Termes IGN] série temporelle
[Termes IGN] température de l'air
[Termes IGN] visualisation de donnéesRésumé : (Auteur) Clustering allows considering groups of similar data elements at a higher level of abstraction. This facilitates the extraction of patterns and useful information from large amounts of spatio-temporal data. Till now, most studies have focused on the extraction of patterns from a spatial or a temporal aspect. Here we use the Bregman block average co-clustering algorithm with I-divergence (BBAC_I) to enable the simultaneous analysis of spatial and temporal patterns in geo-referenced time series (time evolving values of a property observed at fixed geographical locations). In addition, we present three geovisualization techniques to fully explore the co-clustering results: heatmaps offer a straightforward overview of the results; small multiples display the spatial and temporal patterns in geographic maps; ringmaps illustrate the temporal patterns associated to cyclic timestamps. To illustrate this study, we used Dutch daily average temperature data collected at 28 weather stations from 1992 to 2011. The co-clustering algorithm was applied hierarchically to understand the spatio-temporal patterns found in the data at the yearly, monthly and daily resolutions. Results pointed out that there is a transition in temperature patterns from northeast to southwest and from ‘cold’ to ‘hot’ years/months/days with only 3 years belonging to ‘cool’ or ‘cold’ years. Because of its characteristics, this newly introduced algorithm can concurrently analyse spatial and temporal patterns by identifying location-timestamp co-clusters that contain values that are similar along both the spatial and the temporal dimensions. Numéro de notice : A2015-590 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2014.994520 En ligne : http://www.tandfonline.com/doi/full/10.1080/13658816.2014.994520 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77876
in International journal of geographical information science IJGIS > vol 29 n° 4 (April 2015) . - pp 624 - 642[article]Linear spectral mixture analysis via multiple-kernel learning for hyperspectral image classification / Keng-Hao Liu in IEEE Transactions on geoscience and remote sensing, vol 53 n° 4 (April 2015)
[article]
Titre : Linear spectral mixture analysis via multiple-kernel learning for hyperspectral image classification Type de document : Article/Communication Auteurs : Keng-Hao Liu, Auteur ; Yen-Yu Lin, Auteur ; Chu-Song Chen, Auteur Année de publication : 2015 Article en page(s) : pp 2254 - 2269 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse linéaire des mélanges spectraux
[Termes IGN] apprentissage automatique
[Termes IGN] classification automatique
[Termes IGN] image hyperspectraleRésumé : (Auteur) Linear spectral mixture analysis (LSMA) has received wide interests for spectral unmixing in the remote sensing community. This paper introduces a framework called multiplekernel learning-based spectral mixture analysis (MKL-SMA) that integrates a newly proposed MKL method into the training process of LSMA. MKL-SMA allows us to adopt a set of nonlinear basis kernels to better characterize the data so that it can enrich the discriminant capability in classification. Because a single kernel is often insufficient to well present all the data characteristics, MKL-SMA has the advantage of providing a broader range of representation flexibilities; it also eases the kernel selection process because the kernel combination parameters can be learned automatically. Unlike most MKL approaches where complex nonlinear optimization problems are involved in their training process, we derived a closed-form solution of the kernel combination parameters in MKL-SMA. Our method is thus efficient for training and easy to implement. The usefulness of MKL-SMA is demonstrated by conducting real hyperspectral image experiments for performance evaluation. Promising results manifest the effectiveness of the proposed MKL-SMA. Numéro de notice : A2015-173 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2358620 Date de publication en ligne : 29/09/2014 En ligne : https://doi.org/10.1109/TGRS.2014.2358620 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75891
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 4 (April 2015) . - pp 2254 - 2269[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015041 RAB Revue Centre de documentation En réserve L003 Disponible Performance analysis of radial basis function networks and multi-layer perceptron networks in modeling urban change: a case study / Hossein Shafizadeh-Moghadam in International journal of geographical information science IJGIS, vol 29 n° 4 (April 2015)
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Titre : Performance analysis of radial basis function networks and multi-layer perceptron networks in modeling urban change: a case study Type de document : Article/Communication Auteurs : Hossein Shafizadeh-Moghadam, Auteur ; Julian Hagenauer, Auteur ; Manuchehr Farajzadeh, Auteur ; Marco Helbich, Auteur Année de publication : 2015 Article en page(s) : pp 606 - 623 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] apprentissage automatique
[Termes IGN] Bombay
[Termes IGN] croissance urbaine
[Termes IGN] fonction de base radiale
[Termes IGN] milieu urbain
[Termes IGN] modèle de simulation
[Termes IGN] Perceptron multicouche
[Termes IGN] performance
[Termes IGN] test de performance
[Termes IGN] urbanisationRésumé : (Auteur) The majority of cities are rapidly growing. This makes the monitoring and modeling of urban change’s spatial patterns critical to urban planners, decision makers, and environment protection activists. Although a wide range of methods exists for modeling and simulating urban growth, machine learning (ML) techniques have received less attention despite their potential for producing highly accurate predictions of future urban extents. The aim of this study is to investigate two ML techniques, namely radial basis function network (RBFN) and multi-layer perceptron (MLP) networks, for modeling urban change. By predicting urban change for 2010, the models’ performance is evaluated by comparing results with a reference map and by using a set of pertinent statistical measures, such as average spatial distance deviation and figure of merit. The application of these techniques employs the case study area of Mumbai, India. The results show that both models, which were tested using the same explanatory variables, produced promising results in terms of predicting the size and extent of future urban areas. Although a close match between RBFN and MLP is observed, RBFN demonstrates higher spatial accuracy of prediction. Accordingly, RBFN was utilized to simulate urban change for 2020 and 2030. Overall, the study provides evidence that RBFN is a robust and efficient ML technique and can therefore be recommended for land use change modeling. Numéro de notice : A2015-589 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2014.993989 En ligne : http://www.tandfonline.com/doi/full/10.1080/13658816.2014.993989 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77875
in International journal of geographical information science IJGIS > vol 29 n° 4 (April 2015) . - pp 606 - 623[article]Thematic signatures for cleansing and enriching place-related linked data / Benjamin Adams in International journal of geographical information science IJGIS, vol 29 n° 4 (April 2015)
[article]
Titre : Thematic signatures for cleansing and enriching place-related linked data Type de document : Article/Communication Auteurs : Benjamin Adams, Auteur ; Krzysztof Janowicz, Auteur Année de publication : 2015 Article en page(s) : pp 556 - 579 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Infrastructure de données
[Termes IGN] base de connaissances
[Termes IGN] données massives
[Termes IGN] graphe
[Termes IGN] ontologie
[Termes IGN] répertoire toponymique
[Termes IGN] réseau sémantique
[Termes IGN] web des données
[Termes IGN] web sémantiqueRésumé : (Auteur) There has been significant progress transforming semi-structured data about places into knowledge graphs that can be used in a wide variety of geographic information systems such as digital gazetteers or geographic information retrieval systems. For instance, in addition to information about events, actors, and objects, DBpedia contains data about hundreds of thousands of places from Wikipedia and publishes it as Linked Data. Repositories that store data about places are among the most interlinked hubs on the Linked Data cloud. However, most content about places resides in unstructured natural language text, and therefore it is not captured in these knowledge graphs. Instead, place representations are limited to facts such as their population counts, geographic locations, and relations to other entities, for example, headquarters of companies or historical figures. In this paper, we present a novel method to enrich the information stored about places in knowledge graphs using thematic signatures that are derived from unstructured text through the process of topic modeling. As proof of concept, we demonstrate that this enables the automatic categorization of articles into place types defined in the DBpedia ontology (e.g., mountain) and also provides a mechanism to infer relationships between place types that are not captured in existing ontologies. This method can also be used to uncover miscategorized places, which is a common problem arising from the automatic lifting of unstructured and semi-structured data. Numéro de notice : A2015-588 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2014.989855 En ligne : http://www.tandfonline.com/doi/full/10.1080/13658816.2014.989855 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77873
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