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Application of remote sensing to enhance the control of wildlife associated mycobacterium bovis infection / J.S. Mckenzie in Photogrammetric Engineering & Remote Sensing, PERS, vol 68 n° 2 (February 2002)
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Titre : Application of remote sensing to enhance the control of wildlife associated mycobacterium bovis infection Type de document : Article/Communication Auteurs : J.S. Mckenzie, Auteur ; R.S. Morris, Auteur ; D.U. Pfeiffer, Auteur ; J.R. Dymond, Auteur Année de publication : 2002 Article en page(s) : pp 153 - 159 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] biotope
[Termes IGN] cartographie thématique
[Termes IGN] classification
[Termes IGN] image multibande
[Termes IGN] image SPOT
[Termes IGN] prévention des risques
[Termes IGN] risque sanitaireRésumé : (Auteur) The brushtail possum (Trichosurus vulpecula) is a wildlife vector for tuberculosis (TB) caused by Mycobacterium bovis in New Zealand. Supervised automatic classification of a SPOT3 multi spectral image was used to generate a vegetation map, which was used together with slope data to model the risk of TB-infected possums being present in habitat patches. The vegetation data were also used to identify habitat patterns which, together with other geographic variables, were incorporated into logistic regression models to identify predictors of possum TB risk of farms. The impact of the predicted possum TB risk data on the cost-effectiveness of vector control programs at both individual farm and larger regional control areas is discussed, plus issues associated with the uptake of the models by operational managers. Numéro de notice : A2002-013 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans En ligne : https://www.asprs.org/wp-content/uploads/pers/2002journal/february/2002_feb_153- [...] Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=21930
in Photogrammetric Engineering & Remote Sensing, PERS > vol 68 n° 2 (February 2002) . - pp 153 - 159[article]Cloud tracking by scale space classification / D.P. Mukherjee in IEEE Transactions on geoscience and remote sensing, vol 40 n° 2 (February 2002)
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Titre : Cloud tracking by scale space classification Type de document : Article/Communication Auteurs : D.P. Mukherjee, Auteur ; S.T. Acton, Auteur Année de publication : 2002 Article en page(s) : pp 405 - 415 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse spatio-temporelle
[Termes IGN] analyse structurale
[Termes IGN] classification
[Termes IGN] détection de contours
[Termes IGN] nuage
[Termes IGN] séquence d'imagesRésumé : (Auteur) The problem of cloud tracking within a sequence of geo-stationary satellite images has direct relevance to the analysis of cloud life cycles and to the detection of cloud motion vectors (CMVs). The proposed approach first identifies a homogeneous consistent cloud mass for tracking and then establishes motion correspondence within an image sequence. In contrast to the crosscorrelation based approach as adopted in automatic CNIV detection analysis, a scale space classifier is designed to detect cloud mass in the source image taken at time t and the destination image at time t + dt. Boundaries of the extracted cloud segments are matched by computing a correspondence between high curvature points. This shape based method is capable of tracking in the cases of rotation, scaling, and shearing, while the correlation technique is limited to translational motion. The final tracking results provide motion magnitude and direction for each contour point, allowing reliable estimation of meteorological events and wind velocities aloft. With comparable computational expense, the scale space classification technique exceeds the performance of the traditional correlation-based approach in terms of reduced localization error and false matches. Numéro de notice : A2002-099 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/36.992803 En ligne : https://doi.org/10.1109/36.992803 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22014
in IEEE Transactions on geoscience and remote sensing > vol 40 n° 2 (February 2002) . - pp 405 - 415[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-02021 RAB Revue Centre de documentation En réserve L003 Disponible 065-02022 RAB Revue Centre de documentation En réserve L003 Disponible Comparison of GENIE and conventional supervised classifiers for multispectral image feature extraction / N.R. Harvey in IEEE Transactions on geoscience and remote sensing, vol 40 n° 2 (February 2002)
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Titre : Comparison of GENIE and conventional supervised classifiers for multispectral image feature extraction Type de document : Article/Communication Auteurs : N.R. Harvey, Auteur ; et al., Auteur Année de publication : 2002 Article en page(s) : pp 393 - 404 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] classification dirigée
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] extraction semi-automatique
[Termes IGN] image multibandeRésumé : (Auteur) We have developed an automated feature detection/classification system, called GENetic Imagery Exploitation (GENIE), which has been designed to generate image processing pipelines for a variety of feature detection/classification tasks. GENIE is a hybrid evolutionary algorithm that addresses the general problem of finding features of interest in multispectral remotely-sensed images. We describe our system in detail together with experiments involving comparisons of GENIE with several conventional supervised classification techniques, for a number of classification tasks using multispectral remotely sensed imagery. Numéro de notice : A2002-098 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/36.992801 En ligne : https://doi.org/10.1109/36.992801 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22013
in IEEE Transactions on geoscience and remote sensing > vol 40 n° 2 (February 2002) . - pp 393 - 404[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-02021 RAB Revue Centre de documentation En réserve L003 Disponible 065-02022 RAB Revue Centre de documentation En réserve L003 Disponible A derivative-aided hyperspectral image analysis system for land-cover classification / F. Tsai in IEEE Transactions on geoscience and remote sensing, vol 40 n° 2 (February 2002)
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Titre : A derivative-aided hyperspectral image analysis system for land-cover classification Type de document : Article/Communication Auteurs : F. Tsai, Auteur ; W.D. Philpot, Auteur Année de publication : 2002 Article en page(s) : pp 416 - 425 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de données
[Termes IGN] classification dirigée
[Termes IGN] image hyperspectrale
[Termes IGN] occupation du solRésumé : (Auteur) The large number of spectral bands in hyperspectral data seriously complicates their use for classification. Selection of a useful subset of bands or derived features (spectral ratios, differences, derivatives) is always desirable, strongly affects the accuracy of the classification, and is often a practical necessity to keep the processing speed and memory requirements under control. This paper examines one possible procedure for selecting spectral derivatives to improve supervised classification of hyperspectral images. The procedure is designed to identify derivative features that are more effective at separating target classes and then add them to a base subset of features for classification. The goal is to create the smallest set of features that will result in the best classification result. A key issue in this process is the interplay of the number of features and the size of the training data sets since classification accuracy declines if the dimensionality of the feature space is too large relative to the number of training samples. Numéro de notice : A2002-100 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/36.992805 En ligne : https://doi.org/10.1109/36.992805 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22015
in IEEE Transactions on geoscience and remote sensing > vol 40 n° 2 (February 2002) . - pp 416 - 425[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-02021 RAB Revue Centre de documentation En réserve L003 Disponible 065-02022 RAB Revue Centre de documentation En réserve L003 Disponible Fuzzy rule-based classification of remotely sensed imagery / A. Bardossy in IEEE Transactions on geoscience and remote sensing, vol 40 n° 2 (February 2002)
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Titre : Fuzzy rule-based classification of remotely sensed imagery Type de document : Article/Communication Auteurs : A. Bardossy, Auteur ; L. Samaniego, Auteur Année de publication : 2002 Article en page(s) : pp 362 - 374 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Télédétection
[Termes IGN] classification dirigée
[Termes IGN] classification floue
[Termes IGN] image Landsat
[Termes IGN] occupation du sol
[Termes IGN] simulationRésumé : (Auteur) The purpose of this paper is to investigate the applicability of fuzzy rule-based modeling to classify a LANDSAT TM scene from 1984 of an area located in the south of Germany. Both a land cover map with four different categories and an image depicting the degree of ambiguity of the classification for each pixel is the expected output. The fuzzy classification algorithm will use a rule system derived from a training set using simulated annealing as an optimization algorithm. The results are then validated and compared with a common classification method in order to judge the effectiveness of the proposed technique. It will also be shown that the proposed method with only nine rules for four different land cover classes performs slightly better than the maximum likelihood classifier (MLC). For error assessment, the traditional error matrix and fuzzy operators have been used. Numéro de notice : A2002-096 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/36.992798 En ligne : https://doi.org/10.1109/36.992798 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22011
in IEEE Transactions on geoscience and remote sensing > vol 40 n° 2 (February 2002) . - pp 362 - 374[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-02021 RAB Revue Centre de documentation En réserve L003 Disponible 065-02022 RAB Revue Centre de documentation En réserve L003 Disponible Linear spectral random mixture analysis for hyperspectral imagery / C.I. Chang in IEEE Transactions on geoscience and remote sensing, vol 40 n° 2 (February 2002)
PermalinkPermalinkAnalyse d'images aériennes haute résolution pour la reconstruction de scènes urbaines / Matthieu Cord in Bulletin [Société Française de Photogrammétrie et Télédétection], n° 166 (Janvier 2002)
PermalinkAnalyse et segmentation de séquences d'images en vue d'une reconnaissance de formes efficace / Santiago Venegas Martinez (2002)
PermalinkPermalinkApport des SIG et de la télédétection à la détermination d'unités dynamiques des paysages / S. Bancarel (2002)
PermalinkPermalinkComparaison des éléments linéaires de deux bases de données géographiques, version 1.2 / Patrick Marmonier (2002)
PermalinkContribution au développement d'une base de données à référence spatiale pour l'aide à la décision dans la lutte contre la désertification / K. Talbi (2002)
PermalinkEffects of spatial aggregation approaches on classified satellite imagery / H.S. He in International journal of geographical information science IJGIS, vol 16 n° 1 (january 2002)
PermalinkEtude et développement d'un outil d'extraction et d'appariement des éléments caractéristiques du relief à partir de modèles numériques de terrain / E. Colleu (2002)
PermalinkFabrication conjointe de modèles numériques de surface et d'ortho-images pour la visualisation perspective de scènes urbaines / Didier Boldo (2002)
PermalinkGénéralisation et représentation multiple, ch. 10. Mesures et structures d'analyse / Xavier Barillot (2002)
PermalinkGénéralisation et représentation multiple, ch. 11. Analyse des formes des routes / Xavier Barillot (2002)
PermalinkMapping vegetation species succession in a mountainous grassland ecosystem using Landsat, ASTER MI, and Sentinel-2 data / Efosa Gbenga Adagbasa in Plos one, vol 17 n° 1 (January 2022)
PermalinkProceedings of the GIS Research UK, 10th Annual Conference, GISRUK 2002, 3rd - 5th April, University of Sheffield / Steve Wise (2002)
PermalinkScale and texture in digital image classification / J.S. Ferro in Photogrammetric Engineering & Remote Sensing, PERS, vol 68 n° 1 (January 2002)
PermalinkPermalinkA synergic automatic clustering technique (syneract) for multispectral image analysis / K.Y. Huang in Photogrammetric Engineering & Remote Sensing, PERS, vol 68 n° 1 (January 2002)
Permalinkvol 68 n° 1 - January 2002 - The national elevation dataset (Bulletin de Photogrammetric Engineering & Remote Sensing, PERS) / American society for photogrammetry and remote sensing
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