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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 Land 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)
[article]
Titre : Land covers update by supervised classification of segmented ASTER images Type de document : Article/Communication Auteurs : A.R.S. Marcal, Auteur ; J.S. Borges, Auteur ; et al., Auteur Année de publication : 2005 Article en page(s) : pp 1347 - 1362 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse discriminante
[Termes IGN] carte d'occupation du sol
[Termes IGN] classificateur paramétrique
[Termes IGN] classification barycentrique
[Termes IGN] classification dirigée
[Termes IGN] classification floue
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image multibande
[Termes IGN] image Terra-ASTER
[Termes IGN] mise à jour cartographique
[Termes IGN] Portugal
[Termes IGN] segmentation d'imageRésumé : (Auteur) The revision of the 1995 land cover dataset for the Vale do Sousa region, in the northwest of Portugal, was carried out by supervised classification of a multispectral image from the Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) sensor. The nine reflective bands of ASTER were used, covering the spectral range from 0.52-2.43 um. The image was initially ortho-rectified and segmented into 51 186 objects, with an average object size of 135 pixels (about 3 ha). A total of 582 of these objects were identified for training nine land cover classes. The image was classified using an algorithm based on a fuzzy classifier, Support Vector Machines (SVM), K Nearest Neighbours (K-NN) and a Logistic Discrimination (LD) classifier. The results from the classification were evaluated using a set of 277 validation sites, independently gathered. The overall accuracy was 44.6%, for the fuzzy classifier. 70.5%, for the SVM, 60.9% for the K-NN and 72.2% for the LD classifier. The difficulty in discriminating between some of the forest land cover classes was examined by separability analysis and unsupervised classification with hierarchical clustering. The forest classes were found to overlap in the multi-spectral space defined by the nine ASTER bands used. Numéro de notice : A2005-179 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160412331291233 En ligne : https://doi.org/10.1080/01431160412331291233 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27316
in International Journal of Remote Sensing IJRS > vol 26 n° 7 (April 2005) . - pp 1347 - 1362[article]Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité 080-05071 RAB Revue Centre de documentation En réserve L003 Exclu du prêt Satellite image classification using genetically guided fuzzy clustering with spatial information / S. Bandyopadhyay in International Journal of Remote Sensing IJRS, vol 26 n° 3 (February 2005)
[article]
Titre : Satellite image classification using genetically guided fuzzy clustering with spatial information Type de document : Article/Communication Auteurs : S. Bandyopadhyay, Auteur Année de publication : 2005 Article en page(s) : pp 579 - 593 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse de groupement
[Termes IGN] Bombay
[Termes IGN] classification floue
[Termes IGN] classification non dirigée
[Termes IGN] image satellite
[Termes IGN] pixel
[Termes IGN] segmentation d'image
[Termes IGN] utilisation du solRésumé : (Auteur) Land-cover classification of satellite images is an important task in analysis of remote sensing imagery. Segmentation is one of the widely used techniques in this regard. One of the important approaches for segmentation of an image is by clustering the pixels in the spectral domain, where pixels that share some common spectral property are put in the same group, or cluster. However, such spectral clustering completely ignores the spatial information contained in the pixels, which is often an important consideration for good segmentation of images. Moreover, the clustering algorithms often provide locally optimal solutions. In this paper, we propose to perform. image segmentation by a genetically guided unsupervised fuzzy clustering technique where some spatial information of the pixels is incorporated. Two ways of incorporating spatial information are suggested. The characteristic of this technique is that it is able to determine automatically the appropriate number of clusters without making any assumptions regarding the dataset. while attempting to provide globally near optimal solutions. In order to evolve the appropriate number of clusters, the chromosome encoding scheme is enhanced to incorporate the don't care symbol (#). Real-coded genetic algorithm with appropriatly defined operators is used. A cluster validity index is used as a measure of the value of the chromosomes. Results, both quantitative and qualitative are demonstrated for several images, including a satellite image of a part of the city of Mumbai. Numéro de notice : A2005-042 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160512331316432 En ligne : https://doi.org/10.1080/01431160512331316432 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27180
in International Journal of Remote Sensing IJRS > vol 26 n° 3 (February 2005) . - pp 579 - 593[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 080-05031 RAB Revue Centre de documentation En réserve L003 Disponible Alternative representations of in-stream habitat: classification using remote sensing, hydraulic modelling, and fuzzy logic / C. Legleiter in International journal of geographical information science IJGIS, vol 19 n° 1 (january 2005)
[article]
Titre : Alternative representations of in-stream habitat: classification using remote sensing, hydraulic modelling, and fuzzy logic Type de document : Article/Communication Auteurs : C. Legleiter, Auteur ; Michael F. Goodchild, Auteur Année de publication : 2005 Article en page(s) : pp 29 - 50 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Information géographique
[Termes IGN] classification floue
[Termes IGN] cours d'eau
[Termes IGN] écosystème
[Termes IGN] habitat animal
[Termes IGN] incertitude des données
[Termes IGN] limite indéterminée
[Termes IGN] logique floue
[Termes IGN] modèle hydrographique
[Termes IGN] zone inondableRésumé : (Auteur) Improved techniques are needed to characterize complex fluvial systems and monitor ecologically important, yet highly vulnerable riverine environments. This paper explores potential alternatives to traditional mapping of in-stream habitat and presents fuzzy set theory as a means of departing from the rigid, Boolean, object-based framework. We utilize hydrodynamic modeling, remotely sensed data, and fuzzy clustering to obtain classifications that allow for continuous partial membership and gradual transitions among habitat types. Methods of assessing cluster validity are available, but data quality is a crucial consideration. Crisp, vector-based representations can be derived from raster fuzzy classifications by applying a threshold to maximum membership values. This process results in conditional objects separated by ambiguous transition zones, and a compromise must be reached between the proportion of the channel assigned to polygons and the certainty with which this assignment can be made. Spatial patterns of classification uncertainty can also be used to identify areas of confusion, infer boundaries of variable width, and highlight areas of increased habitat diversity. Hydraulic modeling and remote sensing complement one another and, together with field work, could provide a more realistic representation of the fluvial environment. Numéro de notice : A2005-020 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658810412331280220 En ligne : https://doi.org/10.1080/13658810412331280220 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27159
in International journal of geographical information science IJGIS > vol 19 n° 1 (january 2005) . - pp 29 - 50[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-05011 RAB Revue Centre de documentation En réserve L003 Disponible 079-05012 RAB Revue Centre de documentation En réserve L003 Disponible Interactive and visual fuzzy classification of remotely sensed imagery for exploration of uncertainty / Arko Lucieer in International journal of geographical information science IJGIS, vol 18 n° 5 (august 2004)
[article]
Titre : Interactive and visual fuzzy classification of remotely sensed imagery for exploration of uncertainty Type de document : Article/Communication Auteurs : Arko Lucieer, Auteur ; Menno-Jan Kraak, Auteur Année de publication : 2004 Article en page(s) : pp 491 - 512 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification floue
[Termes IGN] image Landsat-ETM+
[Termes IGN] incertitude des données
[Termes IGN] interactivité
[Termes IGN] visualisationRésumé : (Auteur) In this study, we propose, describe, and demonstrate a new geovisualization tool to demonstrate the use of exploratory and interactive visualization techniques for a visual fuzzy classification of remotely sensed imagery. The proposed tool uses dynamically linked views, consisting of an image display, a parallel coordinate plot, a 3D feature space plot, and a classified map with an uncertainty map. It allows a geoscientist to interact with the parameters of a fuzzy classification algorithm by visually adjusting fuzzy membership functions and fuzzy transition zones of land-cover classes. The purpose of this tool is to improve insight into fuzzy classification of remotely sensed imagery and related uncertainty. We tested our tool with a visual fuzzy land-cover classification of a Landsat 7 ETM + image of an area ln southern France characterized by objects with indeterminate boundaries. Good results were obtained with the visual classifier. Additionally, a focus-group user test of the tool showed that insight into a fuzzy classification algorithm and classification uncertainty improved considerably. Numéro de notice : A2004-284 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/13658810410001658094 En ligne : https://doi.org/10.1080/13658810410001658094 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26811
in International journal of geographical information science IJGIS > vol 18 n° 5 (august 2004) . - pp 491 - 512[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-04051 RAB Revue Centre de documentation En réserve L003 Disponible Areas of fuzzy geographical entities / Cidália Costa Fonte in International journal of geographical information science IJGIS, vol 18 n° 2 (march 2004)PermalinkA double continuous approach to visualization and analysis of categorial maps / T. Hengl in International journal of geographical information science IJGIS, vol 18 n° 2 (march 2004)PermalinkMulti-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information / U.C. Benz in ISPRS Journal of photogrammetry and remote sensing, vol 58 n° 3-4 (January - June 2004)PermalinkLinear features extraction in rain forest context from interferometric SAR images by fusion of coherence and amplitude information / V.P. Onana in IEEE Transactions on geoscience and remote sensing, vol 41 n° 11 (November 2003)PermalinkA combined fuzzy pixel-based and object-based approach for classification of high-resolution multispectral data over urban areas / A.K. Shackelford in IEEE Transactions on geoscience and remote sensing, vol 41 n° 10 (October 2003)PermalinkMultitemporal/multiband SAR classification of urban areas using spatial analysis: statistical versus neural kernel-based approach / T. Macri Pellizzei in IEEE Transactions on geoscience and remote sensing, vol 41 n° 10 (October 2003)PermalinkA hierarchical fuzzy classification approach for high-resolution multispectral data over urban areas / A.K. Shackelford in IEEE Transactions on geoscience and remote sensing, vol 41 n° 9 (September 2003)PermalinkImprovements to urban area characterization using multitemporal and multiangle SAR images / F. Dell'acqua in IEEE Transactions on geoscience and remote sensing, vol 41 n° 9 (September 2003)PermalinkThe use of fully polarimetric information for the fuzzy neural classification of SAR images / C.T. Chen in IEEE Transactions on geoscience and remote sensing, vol 41 n° 9 (September 2003)PermalinkRough and fuzzy geographical data integration / K. Oukbir in International journal of geographical information science IJGIS, vol 17 n° 3 (may 2003)Permalink