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Designing fuzzy rule based classifier using self-organizing feature map for analysis of multispectral satellite images / Nikhil R. Pal in International Journal of Remote Sensing IJRS, vol 26 n° 10 (May 2005)
[article]
Titre : Designing fuzzy rule based classifier using self-organizing feature map for analysis of multispectral satellite images Type de document : Article/Communication Auteurs : Nikhil R. Pal, Auteur ; Arijit Laha, Auteur ; Jyotirmay Das, Auteur Année de publication : 2005 Article en page(s) : pp 2219 - 2240 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] carte de Kohonen
[Termes IGN] classificateur
[Termes IGN] classification floue
[Termes IGN] image multibande
[Termes IGN] raisonnement flouRésumé : (Auteur) We propose a novel scheme for designing fuzzy rule based classifiers. A selforganizing feature map (SOFM) based method is used for generating a set of prototypes, which is used to generate a set of fuzzy rules. Each rule represents a region in the feature space that we call the context of the rule. The rules are tuned with respect to their context. We justified that the reasoning scheme may be different in different contexts leading to context sensitive inferencing. To realize context sensitive inferencing we used a softmin operator with a tuneable parameter. The proposed scheme is tested on several multispectral satellite image datasets and the performance is found to be much better than the results reported in the literature. Numéro de notice : A2005-261 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160500033419 En ligne : https://doi.org/10.1080/01431160500033419 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27397
in International Journal of Remote Sensing IJRS > vol 26 n° 10 (May 2005) . - pp 2219 - 2240[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 080-05101 RAB Revue Centre de documentation En réserve L003 Disponible A quantitative comparison of methods for classifying burned areas with LISS-3 imagery / R.M. Roman-Cuesta in International Journal of Remote Sensing IJRS, vol 26 n° 9 (May 2005)
[article]
Titre : A quantitative comparison of methods for classifying burned areas with LISS-3 imagery Type de document : Article/Communication Auteurs : R.M. Roman-Cuesta, Auteur ; J. Retana, Auteur ; et al., Auteur Année de publication : 2005 Article en page(s) : pp 1979 - 2003 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse comparative
[Termes IGN] analyse multibande
[Termes IGN] classificateur paramétrique
[Termes IGN] classification dirigée
[Termes IGN] image IRS-LISS
[Termes IGN] impact sur l'environnement
[Termes IGN] incendie de forêt
[Termes IGN] surveillance écologiqueRésumé : (Auteur) Environmental agencies frequently require tools for quick assessments of areas affected by large fires. Remote sensing techniques have been reported as efficient tools to evaluate the effects of fire. However, there exist few quantitative comparisons about the performance of the diverse methods. This study quantitatively evaluated the accuracy of five different techniques, a field survey and four satellite-based techniques, in order to quickly classify a large forest fire that occurred in 1998 in Solsonès (north-east Spain) by means of an IRS LISS-3 image. Three pure classes were determined: burned area, unburned vegetation, and bare soil; along with a non-pure class that we called mixed area. These selected techniques were included into a tree classifier to investigate their partial contribution to the final classification. The most accurate methods when focusing on pure classes were those directly related to the spectral characteristics of the pixel: Reflectance Data and Spectral Unmixing (82% of overall accuracy), versus the poorer performances of Vegetation Indices (70%), Textural measures (72%) and the field survey (68.6%). Since no image processing technique was applied to the Raw Reflectance Data, it can be considered the most cost-effective method, and the tree classifier reinforces its importance. The results of this study reveal that time consuming and expensive methods are not necessarily the most accurate, especially when potentially easily distinguishable classes are involved. Numéro de notice : A2005-258 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160512331299315 En ligne : https://doi.org/10.1080/01431160512331299315 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27394
in International Journal of Remote Sensing IJRS > vol 26 n° 9 (May 2005) . - pp 1979 - 2003[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 080-05091 RAB Revue Centre de documentation En réserve L003 Exclu du prêt 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)
[article]
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)
[article]
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 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)
[article]
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
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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)PermalinkA 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)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)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)Permalink