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Radial 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)
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Titre : Radial basis function neural networks classification using very high spatial resolution satellite imagery: an application to the habitat area of Lake Kerkini (Greece) Type de document : Article/Communication Auteurs : Iphigenia Keramitsoglou, Auteur ; H. Sarimveis, Auteur ; et al., Auteur Année de publication : 2005 Article en page(s) : pp 1861 - 1880 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse texturale
[Termes IGN] bande spectrale
[Termes IGN] classificateur paramétrique
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] classification par réseau neuronal
[Termes IGN] fonction de base radiale
[Termes IGN] Grèce
[Termes IGN] image à très haute résolution
[Termes IGN] lacRésumé : (Auteur) This study investigates the potential of applying the radial basis function (RBF) neural network architecture for the classification of multispectral very high spatial resolution satellite images into 13 classes of various scales. For the development of the RBF classifiers, the innovative fuzzy means training algorithm is utilized, which is based on a fuzzy partition of the input space. The method requires only a short amount of time to select both the structure and the parameters of the RBF classifier. The new technique was applied to the area of Lake Kerkini, which is a wetland of great ecological value, located in northern Greece. Eleven experiments were carried out in total in order to investigate the performance of the classifier using different input parameters (spectral and textural) as well as different window sizes and neural network complexities. For comparison purposes the same satellite scene was classified using the maximum likelihood (MLH) classification with the same set of training samples. Overall, the neural network classifiers outperformed the MLH classification by 10-17%, reaching a maximum overall accuracy of 78%. Analysis showed that the selection of input parameters is vital for the success of the classifiers. On the other hand, the incorporation of textural analysis and/or modification of the window size do not affect the performance substantially. Numéro de notice : A2005-255 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160512331326594 En ligne : https://doi.org/10.1080/01431160512331326594 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27391
in International Journal of Remote Sensing IJRS > vol 26 n° 9 (May 2005) . - pp 1861 - 1880[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 080-05091 RAB Revue Centre de documentation En réserve L003 Exclu du prêt Satellite remote sensing for detailed landslide inventories using change detection and image fusion / J. Nichol in International Journal of Remote Sensing IJRS, vol 26 n° 9 (May 2005)
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Titre : Satellite remote sensing for detailed landslide inventories using change detection and image fusion Type de document : Article/Communication Auteurs : J. Nichol, Auteur ; M.S. Wong, Auteur Année de publication : 2005 Article en page(s) : pp 1913 - 1926 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] détection de changement
[Termes IGN] effondrement de terrain
[Termes IGN] fusion d'images
[Termes IGN] image Ikonos
[Termes IGN] image multitemporelle
[Termes IGN] image SPOT XS
[Termes IGN] surveillance géologiqueRésumé : (Auteur) The availability of high spatial and spectral resolution remote sensing systems may be accompanied by changes in techniques for applying the data if appropriate data processing methodologies can be demonstrated. Landslide monitoring, which requires large areas to be surveyed at a detailed level, has previously been unsatisfactory due to its reliance on air photograph interpretation. This study demonstrates the synergistic use of medium resolution, multitemporal Satellite pour I'Observation de la Terre (SPOT) XS, and fine resolution IKONOS images for landslide inventories. The post-classification comparison method of change detection using the Maximum Likelihood classifier with SPOT XS images was able to detect approximately 70% of landslides, the main omissions being those smaller than approximately half a pixel wide. The visual quality of images obtained from Pan-sharpening of IKONOS images was comparable to that obtainable from 1:10000 scale air photographs, enabling detailed interpretation of landslides and associated environmental features. A methodology combining the two levels of survey is proposed for regional scale landslide monitoring. Numéro de notice : A2005-257 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160512331314047 En ligne : https://doi.org/10.1080/01431160512331314047 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27393
in International Journal of Remote Sensing IJRS > vol 26 n° 9 (May 2005) . - pp 1913 - 1926[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 080-05091 RAB Revue Centre de documentation En réserve L003 Exclu du prêt A layered stereo matching algorithm using segmentation and global visibility constraints / M. Bleyer in ISPRS Journal of photogrammetry and remote sensing, vol 59 n° 3 (May 2005)
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Titre : A layered stereo matching algorithm using segmentation and global visibility constraints Type de document : Article/Communication Auteurs : M. Bleyer, Auteur ; M. Gelautz, Auteur Année de publication : 2005 Article en page(s) : pp 128 - 150 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] algorithme de décalage moyen
[Termes IGN] algorithme glouton
[Termes IGN] appariement de données localisées
[Termes IGN] compensation par moindres carrés
[Termes IGN] couche thématique
[Termes IGN] détection de partie cachée
[Termes IGN] extraction de couche
[Termes IGN] fenêtre (informatique)
[Termes IGN] graphe planaire
[Termes IGN] programmation par contraintes
[Termes IGN] segmentation d'image
[Termes IGN] visibilité
[Termes IGN] visualisation 3D
[Termes IGN] zone tamponRésumé : (Auteur) This work describes a stereo algorithm that takes advantage of image segmentation, assuming that disparity varies smoothly inside a segment of homogeneous colour and depth discontinuities coincide with segment borders. Image segmentation allows our method to generate correct disparity estimates in large untextured regions and precisely localize depth boundaries. The disparity inside a segment is represented by a planar equation. To derive the plane model, an initial disparity map is generated. We use a window-based approach that exploits the results of segmentation. The size of the match window is chosen adaptively. A segment's planar model is then derived by robust least squared error fitting using the initial disparity map. In a layer extraction step, disparity segments that are found to be similar according to a plane dissimilarity measurement are combined to form a single robust layer. We apply a modified mean-shift algorithm to extract clusters of similar disparity segments. Segments of the same cluster build a layer, the plane parameters of which are computed from its spatial extent using the initial disparity map. We then optimize the assignment of segments to layers using a global cost function. The quality of the disparity map is measured by warping the reference image to the second view and comparing it with the real image. Z-buffering enforces visibility and allows the explicit detection of occlusions. The cost function measures the colour dissimilarity between the warped and real views, and penalizes occlusions and neighbouring segments that are assigned to different layers. Since the problem of finding the assignment of segments to layers that minimizes this cost function is NP-complete, an efficient greedy algorithm is applied to find a local minimum. Layer extraction and assignment are alternately applied. Qualitative and quantitative results obtained for benchmark image pairs show that the proposed algorithm outperforms most state-of-the-art matching algorithms currently listed on the Middlebury stereo evaluation website. The technique achieves particularly good results in areas with depth discontinuities and related occlusions, where missing stereo information is substituted from surrounding regions. Furthermore, we apply the algorithm to a self-recorded image set and show 3D visualizations of the derived results. Numéro de notice : A2005-229 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2005.02.008 En ligne : https://doi.org/10.1016/j.isprsjprs.2005.02.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27366
in ISPRS Journal of photogrammetry and remote sensing > vol 59 n° 3 (May 2005) . - pp 128 - 150[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-05011 SL Revue Centre de documentation Revues en salle Disponible Representing 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)
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Titre : Representing and reducing error in natural-resource classification using model combination Type de document : Article/Communication Auteurs : Zhi Huang, Auteur Année de publication : 2005 Article en page(s) : pp 603 - 621 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse comparative
[Termes IGN] classification de Dempster-Shafer
[Termes IGN] classification par arbre de décision
[Termes IGN] classification par réseau neuronal
[Termes IGN] erreur d'attribut
[Termes IGN] erreur d'échantillon
[Termes IGN] précision de la classification
[Termes IGN] propagation d'erreur
[Termes IGN] ressources naturellesRésumé : (Auteur) Artificial Intelligence (AI) models such as Artificial Neural Networks (ANNs), Decision Trees and Dempster-Shafer's Theory of Evidence have long claimed to be more error-tolerant than conventional statistical models, but the way error is propagated through these models is unclear. Two sources of error have been identified in this study: sampling error and attribute error. The results show that these errors propagate differently through the three AI models. The Decision Tree was the most affected by error, the Artificial Neural Network was less affected by error, and the Theory of Evidence model was not affected by the errors at all. The study indicates that AI models have very different modes of handling errors. In this case, the machine-learning models, including ANNs and Decision Trees, are more sensitive to input errors. Dempster-Shafer's Theory of Evidence has demonstrated better potential in dealing with input errors when multisource data sets are involved. The study suggests a strategy of combining AI models to improve classification accuracy. Several combination approaches have been applied, based on a 'majority voting system', a simple average, Dempster-Shafer's Theory of Evidence, and fuzzy-set theory. These approaches all increased classification accuracy to some extent. Two of them also demonstrated good performance in handling input errors. Second-stage combination approaches which use statistical evaluation of the initial combinations are able to further improve classification results. One of these second-stage combination approaches increased the overall classification accuracy on forest types to 54% from the original 46.5% of the Decision Tree model, and its visual appearance is also much closer to the ground data. By combining models, it becomes possible to calculate quantitative confidence measurements for the classification results, which can then serve as a better error representation. Final classification products include not only the predicted hard classes for individual cells, but also estimates of the probability and the confidence measurements of the prediction. Numéro de notice : A2005-239 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/13658810500032446 En ligne : https://doi.org/10.1080/13658810500032446 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27376
in International journal of geographical information science IJGIS > vol 19 n° 5 (may 2005) . - pp 603 - 621[article]Réservation
Réserver ce documentExemplaires(2)
Code-barres Cote Support Localisation Section Disponibilité 079-05051 RAB Revue Centre de documentation En réserve L003 Disponible 079-05052 RAB Revue Centre de documentation En réserve L003 Disponible A method for detecting large-scale forest covers change using coarse spatial resolution imagery / R.H. Fraser in Remote sensing of environment, vol 95 n° 4 (30/04/2005)
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Titre : A method for detecting large-scale forest covers change using coarse spatial resolution imagery Type de document : Article/Communication Auteurs : R.H. Fraser, Auteur ; A. Abuelgasim, Auteur ; R. Latifovic, Auteur Année de publication : 2005 Article en page(s) : pp 414 - 427 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Canada
[Termes IGN] classificateur paramétrique
[Termes IGN] classification par arbre de décision
[Termes IGN] couvert forestier
[Termes IGN] détection de changement
[Termes IGN] données auxiliaires
[Termes IGN] grande échelle
[Termes IGN] image à basse résolution
[Termes IGN] image à moyenne résolution
[Termes IGN] image NOAA-AVHRR
[Termes IGN] image SPOT-Végétation
[Termes IGN] modèle de régression
[Termes IGN] régression
[Termes IGN] surveillance forestièreRésumé : (Auteur) Many large countries, including Canada, rely on earth observation as a practical and cost-effective means of monitoring their vast inland ecosystems. A potentially efficient approach is one that detects vegetation changes over a hierarchy of spatial scales ranging from coarse to fine. This paper presents a Change Screening Analysis Technique (Change-SAT) designed as a coarse filter to identify the location and timing of large (>5-1 0 kM2) forest cover changes caused by anthropogenic and natural disturbances at an annual, continental scale. The method uses change metrics derived from 1-km multi-temporal SPOT VEGETATION and NOAA AVHRR imagery (reflectance, temperature, and texture information) and ancillary spatial variables (proximity to active fires, roads, and forest tenures) in combination with logistic regression and decision tree classifiers. Major forest changes of interest include wildfires, insect defoliation, forest harvesting and flooding. Change-SAT was tested for 1998-2000 using an independent sample of change and no-change sites over Canada. Overall accuracy was 94% and commission error, especially critical for large-area change applications, was less than 1%. Regions identified as having major or widespread changes could be targeted for more detailed investigation and mapping using field visits, aerial survey or fine resolution EO methods, such as those being applied under Canadian monitoring programs. This multi-resolution approach could be used as pan of a forest monitoring system to report on carbon stocks and forest stewardship. Numéro de notice : A2005-186 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2004.12.014 En ligne : https://doi.org/10.1016/j.rse.2004.12.014 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27323
in Remote sensing of environment > vol 95 n° 4 (30/04/2005) . - pp 414 - 427[article]A comparison of local variance, fractal dimension, and Moran's index as aids to multispectral image classification / C.W. Emerson in International Journal of Remote Sensing IJRS, vol 26 n° 8 (April 2005)
PermalinkRapid response for cloud monitoring through Meteosat VIS-IR and NOAA-A/TOVS image fusion: civil application. A first approach to MSG-SEVIRI / C. Casanova in International Journal of Remote Sensing IJRS, vol 26 n° 8 (April 2005)
PermalinkSignature extension through space for northern landcover classification: a comparison of radiometric correction methods / I. Olthof in Remote sensing of environment, vol 95 n° 3 (15/04/2005)
PermalinkLand covers update by supervised classification of segmented ASTER images / A.R.S. Marcal in International Journal of Remote Sensing IJRS, vol 26 n° 7 (April 2005)
PermalinkSPOT-4 Vegetation multi-temporal compositing for land cover change studies over tropical regions / João M.B. Carreiras in International Journal of Remote Sensing IJRS, vol 26 n° 7 (April 2005)
PermalinkUpdating land cover classification using a rule-based decision system / Damien Raclot in International Journal of Remote Sensing IJRS, vol 26 n° 7 (April 2005)
PermalinkHierarchical recovery of digital terrain models from single and multiple return lidar data / Y. Hu in Photogrammetric Engineering & Remote Sensing, PERS, vol 71 n° 4 (April 2005)
PermalinkIntegration of spatial and spectral information by means of unsupervised extraction and classification for homogenous objects applied to multispectral and hyperspectral data / L.O. Jimenez in IEEE Transactions on geoscience and remote sensing, vol 43 n° 4 (April 2005)
PermalinkMultivariate analysis and geovisualization with an integrated geographic knowledge discovery approach / D. Guo in Cartography and Geographic Information Science, vol 32 n° 2 (April 2005)
PermalinkUse of the Bradley-Terry model to quantify association in remotely sensed images / Alfred Stein in IEEE Transactions on geoscience and remote sensing, vol 43 n° 4 (April 2005)
PermalinkAutomatic detection of oil spills from SAR images / F. Nirchio in International Journal of Remote Sensing IJRS, vol 26 n° 6 (March 2005)
PermalinkRemote sensing image thresholding methods for determining landslide activity / P.L. Rosin in International Journal of Remote Sensing IJRS, vol 26 n° 6 (March 2005)
PermalinkL'apport des données du satellite SPOT 5 à l'étude des zones humides en Bretagne nord : application au bassin versant du Jaudy-Guindy-Bizien / S. Saloum in Photo interprétation, vol 41 n° 1 (Mars 2005)
PermalinkA Bayesian approach to classification of multiresolution remote sensing data / G. Storvik in IEEE Transactions on geoscience and remote sensing, vol 43 n° 3 (March 2005)
PermalinkClassification orientée objet de la perméabilité des sols en zone urbaine à l'aide d'imagerie très haute résolution et de données laser scanner à Curitiba (Brésil) / A. Karsenty in XYZ, n° 102 (mars - mai 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)
PermalinkPartially supervised classification of remote sensing images through SVM-based probability density estimation / P. Mantero in IEEE Transactions on geoscience and remote sensing, vol 43 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)
PermalinkTyphoon insurance pricing with spatial decision support tools / L. Li in International journal of geographical information science IJGIS, vol 19 n° 3 (march 2005)
PermalinkUtilisation des anomalies morphologiques sur des images à très haute résolution dans la détection de dommages occasionnés par des séismes sur un milieu urbain peu densifié / G. Andre in Photo interprétation, vol 41 n° 1 (Mars 2005)
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