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A novel transductive SVM for semisupervised classification of remote-sensing images / Lorenzo Bruzzone in IEEE Transactions on geoscience and remote sensing, vol 44 n° 11 Tome 2 (November 2006)
[article]
Titre : A novel transductive SVM for semisupervised classification of remote-sensing images Type de document : Article/Communication Auteurs : Lorenzo Bruzzone, Auteur ; M. Chi, Auteur ; Mattia Marconcini, Auteur Année de publication : 2006 Article en page(s) : pp 3363 - 3373 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage dirigé
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] classification semi-dirigée
[Termes IGN] reconnaissance automatiqueRésumé : (Auteur) This paper introduces a semisupervised classification method that exploits both labeled and unlabeled samples for addressing ill-posed problems with support vector machines (SVMs). The method is based on recent developments in statistical learning theory concerning transductive inference and in particular transductive SVMs (TSVMs). TSVMs exploit specific iterative algorithms which gradually search a reliable separating hyperplane (in the kernel space) with a transductive process that incorporates both labeled and unlabeled samples in the training phase. Based on an analysis of the properties of the TSVMs presented in the literature, a novel modified TSVM classifier designed for addressing ill-posed remote-sensing problems is proposed. In particular, the proposed technique: 1) is based on a novel transductive procedure that exploits a weighting strategy for unlabeled patterns, based on a time-dependent criterion; 2) is able to mitigate the effects of suboptimal model selection (which is unavoidable in the presence of small-size training sets); and 3) can address multiclass cases. Experimental results confirm the effectiveness of the proposed method on a set of ill-posed remote-sensing classification problems representing different operative conditions. Copyright IEEE Numéro de notice : A2006-527 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2006.877950 En ligne : https://doi.org/10.1109/TGRS.2006.877950 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28250
in IEEE Transactions on geoscience and remote sensing > vol 44 n° 11 Tome 2 (November 2006) . - pp 3363 - 3373[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-06111B RAB Revue Centre de documentation En réserve L003 Disponible Modelling adaptive, spatially aware, and mobile agents: Elk migration in Yellowstone / David A. Bennett in International journal of geographical information science IJGIS, vol 20 n° 9 (october 2006)
[article]
Titre : Modelling adaptive, spatially aware, and mobile agents: Elk migration in Yellowstone Type de document : Article/Communication Auteurs : David A. Bennett, Auteur ; W. Tang, Auteur Année de publication : 2006 Article en page(s) : pp 1039 - 1066 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] apprentissage automatique
[Termes IGN] Etats-Unis
[Termes IGN] Mammalia
[Termes IGN] migration animale
[Termes IGN] objet mobile
[Termes IGN] parc naturel national
[Termes IGN] système multi-agentsRésumé : (Auteur) The potential utility of agent-based models of adaptive, spatially aware, and mobile entities in geographic and ecological research is considerable. Developing this potential, however, presents significant challenges to geographic information science. Modelling the spatio-temporal behaviour of individuals requires new representational forms that capture how organisms store and use spatial information. New procedures must be developed that simulate how individuals produce bounded knowledge of geographical space through experiential learning, adapt this knowledge to continually changing environments, and apply it to spatial decision-making processes. In this paper, we present a framework for the representation of adaptive, spatially aware, and mobile agents. To provide context to this research, a multiagent model is constructed to simulate the migratory behaviour of elk (Cervus elaphus) on Yellowstone's northern range. In this simulated environment, intelligent agents learn in ways that enable them to mimic real-world behaviours and adapt to changing landscapes. Copyright Taylor & Francis Numéro de notice : A2006-423 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658810600830806 En ligne : https://doi.org/10.1080/13658810600830806 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28147
in International journal of geographical information science IJGIS > vol 20 n° 9 (october 2006) . - pp 1039 - 1066[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-06091 RAB Revue Centre de documentation En réserve L003 Disponible 079-06092 RAB Revue Centre de documentation En réserve L003 Disponible A spatial approach to forest-management optimization: linking GIS and multiple objective genetic algorithms / E.I. Ducheyne in International journal of geographical information science IJGIS, vol 20 n° 8 (september 2006)
[article]
Titre : A spatial approach to forest-management optimization: linking GIS and multiple objective genetic algorithms Type de document : Article/Communication Auteurs : E.I. Ducheyne, Auteur ; R.R. DE Wulf, Auteur ; Bernard De Baets, Auteur Année de publication : 2006 Article en page(s) : pp 917 - 928 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] algorithme génétique
[Termes IGN] forêt
[Termes IGN] forêt tempérée
[Termes IGN] optimisation (mathématiques)
[Termes IGN] outil d'aide à la décision
[Termes IGN] sylvicultureRésumé : (Auteur) Forest-management decision-support systems are largely monolithic structures. Spatial details are left out during the optimization process and are elaborated during the operational planning. This might produce misleading results and plans that are impossible to implement. In this paper, a forest-management spatial decision-support systems is presented, in which spatial formulation needed for wildlife models is included during the optimization process. To this end, a multiple-objective genetic algorithm is combined with a geographical information system. An online spatial evaluation of the objective functions is made possible. This is illustrated by a pilot study in Kirkhill forest, Aberdeen. Copyright Taylor & Francis Numéro de notice : A2006-351 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE Nature : Article DOI : 10.1080/13658810600711287 En ligne : https://doi.org/10.1080/13658810600711287 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28075
in International journal of geographical information science IJGIS > vol 20 n° 8 (september 2006) . - pp 917 - 928[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-06081 RAB Revue Centre de documentation En réserve L003 Disponible 079-06082 RAB Revue Centre de documentation En réserve L003 Disponible Land-cover mapping in the Brazilian amazon using SPOT-4 Vegetation data and machine learning classification methods / João M.B. Carreiras in Photogrammetric Engineering & Remote Sensing, PERS, vol 72 n° 8 (August 2006)
[article]
Titre : Land-cover mapping in the Brazilian amazon using SPOT-4 Vegetation data and machine learning classification methods Type de document : Article/Communication Auteurs : João M.B. Carreiras, Auteur ; J.M.C. Pereira, Auteur ; Y.E. Shimabukuro, Auteur Année de publication : 2006 Article en page(s) : pp 897 - 910 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse diachronique
[Termes IGN] apprentissage automatique
[Termes IGN] carte d'occupation du sol
[Termes IGN] cartographie numérique
[Termes IGN] classification ascendante hiérarchique
[Termes IGN] image SPOT-Végétation
[Termes IGN] Mato Grosso
[Termes IGN] occupation du solRésumé : (Auteur) The main objective of this study is to evaluate the feasibility of deriving a land-cover map of the state of Mato Grosso, Brazil, for the year 2000, using data from the 1 km SPOT-4 VEGETATION (VGT) sensor. For this purpose we used a VGT temporal series of 12 monthly composite images, which were further transformed to physical-meaningful fraction images of vegetation, soil, and shade. Classification of fraction images was implemented using several recent machine learning developments, namely, filtering input training data and probability bagging in a classification tree approach. A 10-fold cross validation accuracy assessment indicates that filtering and probability bagging are effective at increasing overall and class-specific accuracy. Overall accuracy and mean probability of class membership were 0.88 and 0.80, respectively. The map of probability of class membership indicates that the larger errors are associated with cerrado savonna and semi-deciduous forest. Copyright ASPRS Numéro de notice : A2006-313 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.72.8.897 En ligne : https://doi.org/10.14358/PERS.72.8.897 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28037
in Photogrammetric Engineering & Remote Sensing, PERS > vol 72 n° 8 (August 2006) . - pp 897 - 910[article]A support vector method for anomaly detection in hyperspectral imagery / Amit Banerjee in IEEE Transactions on geoscience and remote sensing, vol 44 n° 8 (August 2006)
[article]
Titre : A support vector method for anomaly detection in hyperspectral imagery Type de document : Article/Communication Auteurs : Amit Banerjee, Auteur ; Philippe Burlina, Auteur ; Chris Diehl, Auteur Année de publication : 2006 Article en page(s) : pp 2282 - 2291 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] aide à la décision
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] détection d'erreur
[Termes IGN] détection de cible
[Termes IGN] image hyperspectrale
[Termes IGN] méthode fondée sur le noyau
[Termes IGN] test statistiqueRésumé : (Auteur) This paper presents a method for anomaly detection in hyperspectral images based on the support vector data description (SVDD), a kernel method for modeling the support of a distribution. Conventional anomaly-detection algorithms are based upon the popular Reed-Xiaoli detector. However, these algorithms typically suffer from large numbers of false alarms due to the assumptions that the local background is Gaussian and homogeneous. In practice, these assumptions are often violated, especially when the neighborhood of a pixel contains multiple types of terrain. To remove these assumptions, a novel anomaly detector that incorporates a nonparametric background model based on the SVDD is derived. Expanding on prior SVDD work, a geometric interpretation of the SVDD is used to propose a decision rule that utilizes a new test statistic and shares some of the properties of constant false-alarm rate detectors. Using receiver operating characteristic curves, the authors report results that demonstrate the improved performance and reduction in the false-alarm rate when using the SVDD-based detector on wide-area airborne mine detection (WAAMD) and hyperspectral digital imagery collection experiment (HYDICE) imagery. Copyright IEEE Numéro de notice : A2006-396 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2006.873019 En ligne : https://doi.org/10.1109/TGRS.2006.873019 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28120
in IEEE Transactions on geoscience and remote sensing > vol 44 n° 8 (August 2006) . - pp 2282 - 2291[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-06081 RAB Revue Centre de documentation En réserve L003 Disponible A new method to determine near surface air temperature from satellite observations / Ranjit Singh in International Journal of Remote Sensing IJRS, vol 27 n°12-13-14 (July 2006)PermalinkArtificial neural networks for mapping regional-scale upland vegetation from high spatial resolution imagery / H. Mills in International Journal of Remote Sensing IJRS, vol 27 n° 11 (June 2006)PermalinkSubpixel analysis of Landsat ETM/sup +/ using self-organizing map (SOM) neural networks for urban land cover characterization / S. Lee in IEEE Transactions on geoscience and remote sensing, vol 44 n° 6 (June 2006)PermalinkDetection of ancient settlement mounds: archaeological survey based on the STRM terrain model / B.H. Menze in Photogrammetric Engineering & Remote Sensing, PERS, vol 72 n° 3 (March 2006)PermalinkExamining the use of stored navigation knowledge for neural network based INS/GPS integration / Kai-Wei Chiang in Geomatica, vol 60 n° 1 (March 2006)PermalinkBadly posed classification of remotely sensed images : an experimental comparison of existing data labeling systems / A. Baraldi in IEEE Transactions on geoscience and remote sensing, vol 44 n° 1 (January 2006)PermalinkCAp 2006, 8e conférence francophone sur l'apprentissage automatique, 22 - 24 mai 2006, Trégastel, France / Laurent Miclet (2006)PermalinkCumul de mesures de télémétrie laser sur satellites / Arnaud Pollet (2006)PermalinkPermalinkIntelligence artificielle et jeux / Tristan Cazenave (2006)Permalink