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Multiple support vector machines for land cover change detection: an application for mapping urban extensions / H. Nemmour in ISPRS Journal of photogrammetry and remote sensing, vol 61 n° 2 (November 2006)
[article]
Titre : Multiple support vector machines for land cover change detection: an application for mapping urban extensions Type de document : Article/Communication Auteurs : H. Nemmour, Auteur ; Y. Chibani, Auteur Année de publication : 2006 Article en page(s) : pp 125 - 133 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Alger
[Termes IGN] analyse comparative
[Termes IGN] classification par réseau neuronal
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] détection de changement
[Termes IGN] occupation du sol
[Termes IGN] précision géométrique (imagerie)
[Termes IGN] urbanisationRésumé : (Auteur) The reliability of support vector machines for classifying hyperspectral images of remote sensing has been proven in various studies. In this paper, we investigate their applicability for land cover change detection. First, SVM-based change detection is presented and performed for mapping urban growth in the Algerian capital. Different performance indicators, as well as a comparison with artificial neural networks, are used to support our experimental analysis. In a second step, a combination framework is proposed to improve change detection accuracy. Two combination rules, namely, Fuzzy Integral and Attractor Dynamics, are implemented and evaluated with respect to individual SVMs. Recognition rates achieved by individual SVMs, compared to neural networks, confirm their efficiency for land cover change detection. Furthermore, the relevance of SVM combination is highlighted. Copyright ISPRS Numéro de notice : A2006-531 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2006.09.004 En ligne : https://doi.org/10.1016/j.isprsjprs.2006.09.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28254
in ISPRS Journal of photogrammetry and remote sensing > vol 61 n° 2 (November 2006) . - pp 125 - 133[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-06081 SL Revue Centre de documentation Revues en salle Disponible Satellite image classification using granular neural networks / D. Stathakis in International Journal of Remote Sensing IJRS, vol 27 n°18 - 19 - 20 (October 2006)
[article]
Titre : Satellite image classification using granular neural networks Type de document : Article/Communication Auteurs : D. Stathakis, Auteur ; A. Vasilakos, Auteur Année de publication : 2006 Article en page(s) : pp 3991 - 4003 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] classification floue
[Termes IGN] classification par réseau neuronal
[Termes IGN] granularité d'image
[Termes IGN] image IRS-LISSRésumé : (Auteur) The increased synergy between neural networks (NN) and fuzzy sets has led to the introduction of granular neural networks (GNNs) that operate on granules of information, rather than information itself. The fact that processing is done on a conceptual rather than on a numerical level, combined with the representation of granules using linguistic terms, results in increased interpretability. This is the actual benefit, and not increased accuracy, gained by GNNs. The constraints used to implement the GNN are such that accuracy degradation should not be surprising. Having said that, it is well known that simple structured NNs tend to be less prone to over-fitting the training data set, maintaining the ability to generalize and more accurately classify previously unseen data. Standard NNs are frequently found to be accurate but difficult to explain, hence they are often associated with the black box syndrome. Because in GNNs the operation is carried out at a conceptual level, the components have unambiguous meaning, revealing how classification decisions are formed. In this paper, the interpretability of GNNs is exploited using a satellite image classification problem. We examine how land use classification using both spectral and non-spectral information is expressed in GNN terms. One further contribution of this paper is the use of specific symbolization of the network components to easily establish causality relationships. Copyright Taylor & Francis Numéro de notice : A2006-460 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160600567779 En ligne : https://doi.org/10.1080/01431160600567779 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28184
in International Journal of Remote Sensing IJRS > vol 27 n°18 - 19 - 20 (October 2006) . - pp 3991 - 4003[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 080-06101 RAB Revue Centre de documentation En réserve L003 Disponible Comparison of computational intelligence based classification techniques for remotely sensed optical image classification / D. Stathakis in IEEE Transactions on geoscience and remote sensing, vol 44 n° 8 (August 2006)
[article]
Titre : Comparison of computational intelligence based classification techniques for remotely sensed optical image classification Type de document : Article/Communication Auteurs : D. Stathakis, Auteur ; A. Vasilakos, Auteur Année de publication : 2006 Article en page(s) : pp 2305 - 2318 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] analyse comparative
[Termes IGN] classification dirigée
[Termes IGN] classification floue
[Termes IGN] classification par algorithme génétique
[Termes IGN] classification par réseau neuronal
[Termes IGN] image optique
[Termes IGN] occupation du solRésumé : (Auteur) Several computational intelligence components, namely neural networks (NNs), fuzzy sets, and genetic algorithms (GAs), have been applied separately or in combination to the process of remotely sensed data classification. By applying computational intelligence, we expect increased accuracy through the use of NNs, optimal NN structure and parameter determination via GAs, and transparency using fuzzy sets is expected. This paper systematically reviews and compares several configurations in the particular context of remote sensing for land cover. In addition, some of the configurations used here, such as NEFCASS and CANFIS, have few previous applications in the field. A comparison of the configurations is achieved by testing the different methods with exactly the same case-study data. A thorough assessment of results is performed by constructing an accuracy matrix for each training and testing data set. The evaluation of different methods is not only based on accuracy but also on compactness, completeness, and consistency. The architecture, produced rule set, and training parameters for the specific classification task are presented. Some comments and directions for future work are given. Copyright IEEE Numéro de notice : A2006-397 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2006.872903 En ligne : https://doi.org/10.1109/TGRS.2006.872903 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28121
in IEEE Transactions on geoscience and remote sensing > vol 44 n° 8 (August 2006) . - pp 2305 - 2318[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-06081 RAB Revue Centre de documentation En réserve L003 Disponible Tree cover and height estimation in the Fennoscandian tundra-taiga transition zone using multiangular MISR data / J. Heiskanen in Remote sensing of environment, vol 103 n° 1 (15 July 2006)
[article]
Titre : Tree cover and height estimation in the Fennoscandian tundra-taiga transition zone using multiangular MISR data Type de document : Article/Communication Auteurs : J. Heiskanen, Auteur Année de publication : 2006 Article en page(s) : pp 97 - 114 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] arbre (flore)
[Termes IGN] classification par réseau neuronal
[Termes IGN] détection de changement
[Termes IGN] Finlande
[Termes IGN] forêt boréale
[Termes IGN] image Terra-MISR
[Termes IGN] taïga
[Termes IGN] toundraRésumé : (Auteur) The tundra–taiga transition zone stretches around the northern hemisphere separating boreal forest to the south from treeless tundra to the north. Tree cover and height are important variables to characterize this vegetation transition. Accurate continuous fields of tree cover and height would enable the delineation of the forest extent according to different criterion and provide useful data for change detection of this climatically sensitive ecotone. This study examined if multiangular remote sensing data has potential to improve the accuracy of the tree cover and height estimates in relation to nadir-view data. The satellite data consisted of Multi-angle Imaging SpectroRadiometer (MISR) data at 275 m and 1.1 km resolutions. The study area was located in the Fennoscandian tundra–taiga transition zone, in northernmost Finland. The continuous fields of tree cover and height were estimated using neural networks, which were trained and assessed by high-resolution biotope inventory data. The spectral–angular data together produced lower estimation errors than single band nadir, multispectral nadir or single band multiangular data alone. RMSE of the tree cover estimates reduced from 7.8% (relative RMSE 67.4%) to 6.5% (56.1%) at 275 m resolution, and from 5.4% (49.2%) to 4.1% (36.9%) at 1.1 km resolution, when multispectral nadir data were used together with multiangular data. RMSE of the tree height estimates reduced from 2.3 m (44.3%) to 2.0 m (37.6%) and from 1.8 m (35.4%) to 1.3 m (25.4%), respectively. The largest estimation errors occurred in mires and in areas of dense shrub cover, but the use of multiangular data also reduced estimation errors in these areas. The results suggest that directional information has potential to improve the tree cover and height estimates, and hence the accuracy of the land cover change detection in the tundra–taiga transition zone. Copyright Elsevier Numéro de notice : A2006-285 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2006.03.015 En ligne : https://doi.org/10.1016/j.rse.2006.03.015 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28012
in Remote sensing of environment > vol 103 n° 1 (15 July 2006) . - pp 97 - 114[article]Some issues in the classification of DAIS hyperspectral data / M. Pal in International Journal of Remote Sensing IJRS, vol 27 n°12-13-14 (July 2006)
[article]
Titre : Some issues in the classification of DAIS hyperspectral data Type de document : Article/Communication Auteurs : M. Pal, Auteur ; Paul M. Mather, Auteur Année de publication : 2006 Article en page(s) : pp 2895 - 2916 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] classificateur paramétrique
[Termes IGN] classification dirigée
[Termes IGN] classification par arbre de décision
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] classification par réseau neuronal
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] Espagne
[Termes IGN] image DAIS
[Termes IGN] image hyperspectrale
[Termes IGN] précision de la classification
[Termes IGN] qualité du processus
[Termes IGN] transformation orthogonaleRésumé : (Auteur) Classification accuracy depends on a number of factors, of which the nature of the training samples, the number of bands used, the number of classes to be identified relative to the spatial resolution of the image and the properties of the classifier are the most important. This paper evaluates the effects of these factors on classification accuracy using a test area in La Mancha, Spain. High spectral and spatial resolution DAIS data were used to compare the performance of four classification procedures (maximum likelihood, neural network, support vector machines and decision tree). There was no evidence to support the view that classification accuracy inevitably declines as the data dimensionality increases. The support vector machine classifier performed well with all test data sets. The use of the orthogonal MNF transform resulted in a decline in classification accuracy. However, the decision-tree approach to feature selection worked well. Small increases in classifier accuracy may be obtained using more sophisticated techniques, but it is suggested here that greater attention should be given to the collection of training and test data that represent the range of land surface variability at the spatial scale of the image. Copyright Taylor & Francis Numéro de notice : A2006-309 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160500185227 En ligne : https://doi.org/10.1080/01431160500185227 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28033
in International Journal of Remote Sensing IJRS > vol 27 n°12-13-14 (July 2006) . - pp 2895 - 2916[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 080-06071 RAB Revue Centre de documentation En réserve L003 Disponible Artificial 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)PermalinkIntegrating LIDAR elevation data, multi-spectral imagery and neural network modelling for marsh characterization / J.T. Morris in International Journal of Remote Sensing IJRS, vol 26 n° 23 (December 2005)PermalinkReconstructing spatiotemporal trajectories from sparse data / P. Partsinevelos in ISPRS Journal of photogrammetry and remote sensing, vol 60 n° 1 (December 2005 - March 2006)PermalinkClassifying and mapping wildfire severity: a comparison of methods / C.K. Brewer in Photogrammetric Engineering & Remote Sensing, PERS, vol 71 n° 11 (November 2005)PermalinkA statistical self-organizing learning system for remote sensing classification / H.M. Chi in IEEE Transactions on geoscience and remote sensing, vol 43 n° 8 (August 2005)PermalinkNouvelle approche du réseau ARTMAP flou : application à la classification multi-spectrale des images SPOT XS de la baie d'Alger / F. Alilat in Revue Française de Photogrammétrie et de Télédétection, n° 177 (Juin 2005)PermalinkRadial basis function neural networks classification using very high spatial resolution satellite imagery: an application to the habitat area of Lake Kerkini (Greece) / Iphigenia Keramitsoglou in International Journal of Remote Sensing IJRS, vol 26 n° 9 (May 2005)PermalinkRepresenting and reducing error in natural-resource classification using model combination / Zhi Huang in International journal of geographical information science IJGIS, vol 19 n° 5 (may 2005)PermalinkNested hyper-rectangle learning model for remote sensing: land-cover classification / L. Chen in Photogrammetric Engineering & Remote Sensing, PERS, vol 71 n° 3 (March 2005)PermalinkSparse grids: a new predictive modelling method for the analysis of geographic data / S.W. Laffan in International journal of geographical information science IJGIS, vol 19 n° 3 (march 2005)Permalink