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Multispectral land use classification using neural networks and support vector machines: one or the other, or both? / B. Dixon in International Journal of Remote Sensing IJRS, vol 29 n°3-4 (February 2008)
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Titre : Multispectral land use classification using neural networks and support vector machines: one or the other, or both? Type de document : Article/Communication Auteurs : B. Dixon, Auteur ; N. Candade, Auteur Année de publication : 2008 Article en page(s) : pp 1185 - 1206 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse comparative
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] classification par réseau neuronal
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] occupation du solRésumé : (Auteur) Land use classification is an important part of many remote sensing applications. A lot of research has gone into the application of statistical and neural network classifiers to remote-sensing images. This research involves the study and implementation of a new pattern recognition technique introduced within the framework of statistical learning theory called Support Vector Machines (SVMs), and its application to remote-sensing image classification. Standard classifiers such as Artificial Neural Network (ANN) need a number of training samples that exponentially increase with the dimension of the input feature space. With a limited number of training samples, the classification rate thus decreases as the dimensionality increases. SVMs are independent of the dimensionality of feature space as the main idea behind this classification technique is to separate the classes with a surface that maximizes the margin between them, using boundary pixels to create the decision surface. Results from SVMs are compared with traditional Maximum Likelihood Classification (MLC) and an ANN classifier. The findings suggest that the ANN and SVM classifiers perform better than the traditional MLC. The SVM and the ANN show comparable results. However, accuracy is dependent on factors such as the number of hidden nodes (in the case of ANN) and kernel parameters (in the case of SVM). The training time taken by the SVM is several magnitudes less. Copyright Taylor & Francis Numéro de notice : A2008-009 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160701294661 En ligne : https://doi.org/10.1080/01431160701294661 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29004
in International Journal of Remote Sensing IJRS > vol 29 n°3-4 (February 2008) . - pp 1185 - 1206[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 080-08021 RAB Revue Centre de documentation En réserve L003 Disponible The application of artificial neural networks to the analysis of remotely sensed data / J.F. Mas in International Journal of Remote Sensing IJRS, vol 29 n°3-4 (February 2008)
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Titre : The application of artificial neural networks to the analysis of remotely sensed data Type de document : Article/Communication Auteurs : J.F. Mas, Auteur ; J.J. Flores, Auteur Année de publication : 2008 Article en page(s) : pp 617 - 663 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage automatique
[Termes IGN] classification par réseau neuronal
[Termes IGN] image aérienne
[Termes IGN] image satellite
[Termes IGN] réseau neuronal artificielRésumé : (Auteur) Artificial neural networks (ANNs) have become a popular tool in the analysis of remotely sensed data. Although significant progress has been made in image classification based upon neural networks, a number of issues remain to be resolved. This paper reviews remotely sensed data analysis with neural networks. First, we present an overview of the main concepts underlying ANNs, including the main architectures and learning algorithms. Then, the main tasks that involve ANNs in remote sensing are described. The limitations and crucial issues relating to the application of the neural network approach are discussed. A brief review of the implementation of ANNs in some of the most popular image processing software packages is presented. Finally, we discuss the application perspectives of neural networks in remote sensing image analysis. Copyright Taylor & Francis Numéro de notice : A2008-004 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160701352154 En ligne : https://doi.org/10.1080/01431160701352154 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28999
in International Journal of Remote Sensing IJRS > vol 29 n°3-4 (February 2008) . - pp 617 - 663[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 080-08021 RAB Revue Centre de documentation En réserve L003 Disponible Global elevation ancillary data for land-use classification using granular neural networks / D. Stathakis in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 1 (January 2008)
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Titre : Global elevation ancillary data for land-use classification using granular neural networks Type de document : Article/Communication Auteurs : D. Stathakis, Auteur ; I. Kanellopoulos, Auteur Année de publication : 2008 Article en page(s) : pp 55 - 63 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] altitude
[Termes IGN] classification floue
[Termes IGN] classification par réseau neuronal
[Termes IGN] données auxiliaires
[Termes IGN] fusion d'images
[Termes IGN] granularité d'image
[Termes IGN] logique floue
[Termes IGN] utilisation du solRésumé : (Auteur) The development of digital global databases containing data such as elevation and soil can greatly simplify and aid in the classification of remotely sensed data to create land-use classes. An efficient method that can simultaneously handle diverse input dimensions can be formed by merging fuzzy logic and neural networks. The so-called granular or fuzzy neural networks are able not only to achieve high classification levels, but at the same time produce compressed and transparent neural network skeletons. Compression results in reduced training times, while transparency is an aid for interpreting the structure of the neural network by translating it into meaningful rules and vice versa. The purpose of this paper is to provide some initial guidelines for the construction of granular neural networks in the remote sensing context, while using global elevation ancillary data within the classification process. Copyright ASPRS Numéro de notice : A2008-014 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.74.1.55 En ligne : https://doi.org/10.14358/PERS.74.1.55 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29009
in Photogrammetric Engineering & Remote Sensing, PERS > vol 74 n° 1 (January 2008) . - pp 55 - 63[article]Visual analysis of network traffic – interactive monitoring, detection, and interpretation of security threats / Florian Mansmann (ca 2008)
Titre : Visual analysis of network traffic – interactive monitoring, detection, and interpretation of security threats Type de document : Thèse/HDR Auteurs : Florian Mansmann, Auteur Editeur : Konstanz : University of Konstanz Année de publication : ca 2008 Importance : 186 p. Format : 21 x 30 cm Note générale : bibliographie
Dissertation zur Erlangung des akademischen Grades des Doktors der Naturwissenschaften an der Universität Konstanz im Fachbereich Informatik und InformationswissenschaftLangues : Français (fre) Descripteur : [Vedettes matières IGN] Informatique
[Termes IGN] analyse multivariée
[Termes IGN] analyse visuelle
[Termes IGN] carte de Kohonen
[Termes IGN] classification par réseau neuronal
[Termes IGN] géovisualisation
[Termes IGN] graphe
[Termes IGN] internet
[Termes IGN] sécurité informatique
[Termes IGN] surveillance informatiqueRésumé : (auteur) The Internet has become a dangerous place: malicious code gets spread on personal computers across the world, creating botnets ready to attack the network infrastructure at any time. Monitoring network traffic and keeping track of the vast number of security incidents or other anomalies in the network are challenging tasks. While monitoring and intrusion detection systems are widely used to collect operational data in real-time, attempts to manually analyze their output at a fine-granular level are often tedious, require exhaustive human resources, or completely fail to provide the necessary insight due to the complexity and the volume of the underlying data. This dissertation represents an effort to complement automatic monitoring and intrusion detection systems with visual exploration interfaces that empower human analysts to gain deeper insight into large, complex, and dynamically changing data sets. In this context, one key aspect of visual analysis is the refinement of existing visualization methods to improve their scalability with respect to a) data volume, b) visual limitations of computer screens, and c) human perception capacities. In addition to that, developmet of innovative visualization metaphors for viewing network data is a further key aspect of this thesis. In particular, this dissertation deals with scalable visualization techniques for detailed analysis of large network time series. By grouping time series according to their logical intervals in pixel visualizations and by coloring them for better discrimination, our methods enable accurate comparisons of temporal aspects in network security data sets. In order to reveal the peculiarities of network traffic and distributed attacks with regard to the distribution of the participating hosts, a hierarchical map of the IP address space, which takes both geographical and topological aspects of the Internet into account, is proposed. Since visual clutter becomes an issue when naively connecting the major communication partners on top of this map, hierarchical edge bundles are used for grouping traffic links based on the map’s hierarchy, thereby facilitating a more scalable analysis of communication partners. Furthermore, the map is complemented by multivariate analysis techniques for visually studying the multidimensional nature of network traffic and security event data. Especially the interaction of the implemented prototypes reveals the ability of the proposed visualization methods to provide an overview, to relate communication partners, to zoom into regions of interest, and to retrieve detailed information. For an even more detailed analysis of hosts in the network, we introduce a graph-based approach to tracking behavioral changes of hosts and higher-level network entities. This information is particularly useful for detecting misbehaving computers within the local network infrastructure, which can otherwise substantially compromise the security of the network. To complete the comprehensive view on network traffic, a Self-Organizing Map was used to demonstrate the usefulness of visualization methods for analyzing not only structured network protocol data, but also unstructured information, e.g., textual context of email messages. By extracting features from the emails, the neuronal network algorithm clusters similar emails and is capable of distinguishing between spam and legitimate emails up to a certain extent. In the scope of this dissertation, the presented prototypes demonstrate the applicability of the proposed visualization methods in numerous case studies and reveal the exhaustless potential of their usage in combination with automatic detection methods. We are therefore confident that in the fields of network monitoring and security visual analytics applications will quickly find their way from research into practice by combining human background knowledge and intelligence with the speed and accuracy of computers. Numéro de notice : 17246 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Thèse étrangère Note de thèse : Dissertation : Informatique : Constance : 2008 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81637 Border vector detection and adaptation for classification of multispectral and hyperspectral remote sensing images / N.G. Kasapoglu in IEEE Transactions on geoscience and remote sensing, vol 45 n° 12 Tome 1 (December 2007)
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Titre : Border vector detection and adaptation for classification of multispectral and hyperspectral remote sensing images Type de document : Article/Communication Auteurs : N.G. Kasapoglu, Auteur ; O.K. Ersoy, Auteur Année de publication : 2007 Article en page(s) : pp 3880 - 3893 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] apprentissage dirigé
[Termes IGN] classification par arbre de décision
[Termes IGN] classification par réseau neuronal
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] précision de la classificationRésumé : (Auteur) Effective partitioning of the feature space for high classification accuracy with due attention to rare class members is often a difficult task. In this paper, the border vector detection and adaptation (BVDA) algorithm is proposed for this purpose. The BVDA consists of two parts. In the first part of the algorithm, some specially selected training samples are assigned as initial reference vectors called border vectors. In the second part of the algorithm, the border vectors are adapted by moving them toward the decision boundaries. At the end of the adaptation process, the border vectors are finalized. The method next uses the minimum distance to border vector rule for classification. In supervised learning, the training process should be unbiased to reach more accurate results in testing. In the BVDA, decision region borders are related to the initialization of the border vectors and the input ordering of the training samples. Consensus strategy can be applied with cross validation to reduce these dependencies. The performance of the BVDA and consensual BVDA were studied in comparison to other classification algorithms including neural network with backpropagation learning, support vector machines, and some statistical classification techniques. Copyright IEEE Numéro de notice : A2007-582 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2007.900699 En ligne : https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4378538 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28945
in IEEE Transactions on geoscience and remote sensing > vol 45 n° 12 Tome 1 (December 2007) . - pp 3880 - 3893[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-07121A RAB Revue Centre de documentation En réserve L003 Disponible Feature selection by genetic algorithms in object-based classification of Ikonos imagery for forest mapping in Flanders, Belgium / F.M.B. Van Coillie in Remote sensing of environment, vol 110 n° 4 (30/10/2007)
PermalinkMultispectral image classification: a supervised neural computation approach based on rough-fuzzy membership function and weak fuzzy similarity relation / A. Agrawal in International Journal of Remote Sensing IJRS, vol 28 n°19-20 (October 2007)
PermalinkDetection and discrimination between oil spills and look-alike phenomena through neural networks / Konstantinos Topouzelis in ISPRS Journal of photogrammetry and remote sensing, vol 62 n° 4 (September 2007)
PermalinkLe WI-FI pour le positionnement et la navigation en intérieur / A. Betremieux in XYZ, n° 111 (juin - août 2007)
PermalinkAtmospheric correction algorithm for MERIS above case-2 waters / Th. Schroeder in International Journal of Remote Sensing IJRS, vol 28 n°7-8 (April 2007)
PermalinkImproving land-cover classification using recognition threshold neural networks / M.J. Aitkenhead in Photogrammetric Engineering & Remote Sensing, PERS, vol 73 n° 4 (April 2007)
PermalinkMapping land cover from detailed aerial photography data using textural and neural network analysis / R. Cots-Folch in International Journal of Remote Sensing IJRS, vol 28 n°7-8 (April 2007)
PermalinkAn operational MISR pixel classifier using support vector machines / D. Mazzoni in Remote sensing of environment, vol 107 n° 1-2 (15 March 2007)
PermalinkA data-mining approach to associating MISR smoke plume heights with MODIS fire measurements / D. Mazzoni in Remote sensing of environment, vol 107 n° 1-2 (15 March 2007)
PermalinkNeural network estimation of LAI, fAPAR, fCover and LAI*Cab, from top of canopy MERIS reflectance data: principles and validation / Cédric Bacour in Remote sensing of environment, vol 105 n° 4 (30/12/2006)
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