Descripteur
Documents disponibles dans cette catégorie (70)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
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
Réserver ce documentExemplaires (1)
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 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)
[article]
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]Signature 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)
[article]
Titre : Signature extension through space for northern landcover classification: a comparison of radiometric correction methods Type de document : Article/Communication Auteurs : I. Olthof, Auteur ; C. Butson, Auteur ; R. Fraser, Auteur Année de publication : 2005 Article en page(s) : pp 290 - 302 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] agriculture
[Termes IGN] analyse comparative
[Termes IGN] classificateur paramétrique
[Termes IGN] correction radiométrique
[Termes IGN] image Landsat
[Termes IGN] limite de résolution géométrique
[Termes IGN] occupation du sol
[Termes IGN] phénologie
[Termes IGN] prévision
[Termes IGN] signature spectraleRésumé : (Auteur) Northern landcover mapping for climate change and carbon modeling requires greater detail than what is available from coarse resolution data. Mapping landcover with medium resolution data from Landsat presents challenges due to differences in time and space between scene acquisitions required for full coverage. These differences cause landcover signatures to vary due to haze, solar geometry and phenology, among other factors. One way to circumvent this problem is to have an image interpreter classify each scene independently, however, this is not an optimal solution in the north due to a lack of spatially extensive reference data and resources required to label scenes individually. Another possible approach is to stabilize signatures in space and time so that they may be extracted from one scene and extended to others, thereby reducing the amount of reference data and user input required for mapping large areas. A radiometric normalization approach was developed that exploits the high temporal frequency with which coarse resolution data are acquired and the high spatial frequency of medium resolution data. The current paper compares this radiometric correction methodology with an established absolute calibration methodology for signature extension for landcover classification and explores factors that affect extension performance to recommend how and when signature extension can be applied. Overall, the new normalization method produced better extension and classification results than absolute calibration. Results also showed that extension performance was affected more by geographical distance than by differences in anniversary dates between acquisitions for the range of data examined. Geographical distance in the north-south direction leads to poorer extension performance than distance in the cast west direction due in part to differences in vegetation composition assigned the same class label in the latitudinal direction. While extension performance was somewhat variable and in some cases did not produce a best classification result by itself, it provided an initial best guess of landcover that can subsequently be refined by an expert image interpreter. Numéro de notice : A2005-170 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2004.12.015 En ligne : https://doi.org/10.1016/j.rse.2004.12.015 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27308
in Remote sensing of environment > vol 95 n° 3 (15/04/2005) . - pp 290 - 302[article]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)PermalinkExtraction semi-automatique de bâtiments à partir d'images satellitaires / Z. Mabed (2005)PermalinkKnowledge-based approaches to accurate mapping of mangroves from satellite data / J. Gao in Photogrammetric Engineering & Remote Sensing, PERS, vol 70 n° 11 (November 2004)PermalinkNonparametric weighted feature extraction for classification / D.A. Landgrebe in IEEE Transactions on geoscience and remote sensing, vol 42 n° 5 (May 2004)PermalinkClassifying land development in high-resolution panchromatic satellite images using straight-line statistics / C. Unsalan in IEEE Transactions on geoscience and remote sensing, vol 42 n° 4 (April 2004)PermalinkCarbon mass fluxes of forests in Belgium determined with low resolution optical sensors / F. Veroustraete in International Journal of Remote Sensing IJRS, vol 25 n° 4 (February 2004)PermalinkHyperspectral monitoring of physiological parameters of wheat during a vegetation period using AVIS data / N. Oppelt in International Journal of Remote Sensing IJRS, vol 25 n° 1 (January 2004)PermalinkA cost-effective semisupervised classifier approach with kernels / M. Murat Dundar in IEEE Transactions on geoscience and remote sensing, vol 42 n° 1 (January 2004)PermalinkA credit assignment approach to fusing classifiers of multiseason hyperspectral imagery / C. Bachmann in IEEE Transactions on geoscience and remote sensing, vol 41 n° 11 (November 2003)PermalinkA Markov random field approach to spatio-temporal contextual image classification / F. Melgani in IEEE Transactions on geoscience and remote sensing, vol 41 n° 11 (November 2003)Permalink