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An operational MISR pixel classifier using support vector machines / D. Mazzoni in Remote sensing of environment, vol 107 n° 1-2 (15 March 2007)
[article]
Titre : An operational MISR pixel classifier using support vector machines Type de document : Article/Communication Auteurs : D. Mazzoni, Auteur ; M.J. Garay, Auteur ; R. Davies, Auteur ; et al., Auteur Année de publication : 2007 Article en page(s) : pp 149 - 158 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage automatique
[Termes IGN] classification dirigée
[Termes IGN] classification par réseau neuronal
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image Terra-MISRRésumé : (Auteur) The Multi-angle Imaging SpectroRadiometer (MISR) data products now include a scene classification for each 1.1-km pixel that was developed using Support Vector Machines (SVMs), a cutting-edge machine learning technique for supervised classification. Using a combination of spectral, angular, and texture features, each pixel is classified as land, water, cloud, aerosol, or snow/ice, with the aerosol class further divided into smoke, dust, and other aerosols. The classifier was trained by MISR scientists who labeled hundreds of scenes using a custom interactive tool that showed them the results of the training in real time, making the process significantly faster. Preliminary validation shows that the accuracy of the classifier is approximately 81% globally at the 1.1-km pixel level. Applications of this classifier include global studies of cloud and aerosol distribution, as well as data mining applications such as searching for smoke plumes. This is one of the largest and most ambitious operational uses of machine learning techniques for a remote-sensing instrument, and the success of this system will hopefully lead to further use of this approach. Copyright Elsevier Numéro de notice : A2007-054 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2006.06.021 En ligne : https://doi.org/10.1016/j.rse.2006.06.021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28419
in Remote sensing of environment > vol 107 n° 1-2 (15 March 2007) . - pp 149 - 158[article]Comparison between several feature extraction/classification methods for mapping complicated agricultural land use patches using airborne hyperspectral data / S. Lu in International Journal of Remote Sensing IJRS, vol 28 n°5-6 (March 2007)
[article]
Titre : Comparison between several feature extraction/classification methods for mapping complicated agricultural land use patches using airborne hyperspectral data Type de document : Article/Communication Auteurs : S. Lu, Auteur ; K. Oki, Auteur Année de publication : 2007 Article en page(s) : pp 963 - 984 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] agriculture
[Termes IGN] analyse comparative
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification dirigée
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] extraction automatique
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image aérienne
[Termes IGN] image hyperspectrale
[Termes IGN] précision de la classification
[Termes IGN] surface cultivée
[Termes IGN] Tokyo (Japon)
[Termes IGN] utilisation du solRésumé : (Auteur) Airborne hyperspectral remote sensing was applied to agricultural land in the Miura Peninsula, near the metropolis of Tokyo in Japan. The study area is characterized by complicated land use patches, which is the general characteristic of most agricultural lands in Japan. Several feature extraction/classification methods were examined in classifying the land use and plant species. The results showed that decision boundary feature extraction (DBFE) was better than principal component analysis (PCA) as the feature extraction method. Moreover, the pre-classification process using NDVI that separates the whole study area into vegetated area and non-vegetated areas also improved the classification accuracy. After the pre-procedures, the land use and plant species were finally mapped by maximum likelihood classification (MLC) or extraction and classification of homogeneous objects (ECHO). The best kappa (overall accuracy) of classification was 0.914 (92.4%) and 0.924 (93.3%) for MLC and ECHO, respectively. The best accuracies of each category for the image were 79.5% to 100% for plant species (watermelon, pumpkin, marigold, grass and tree), 88.7% to 100% for soil types, 97.8% for concrete, and 99.4% for vinyl-mulches. Although, built-up area has low estimation accuracy, this did not affect the overall classification accuracy because it covers only a very small area. Copyright Taylor & Francis Numéro de notice : A2007-097 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160600771561 En ligne : https://doi.org/10.1080/01431160600771561 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28462
in International Journal of Remote Sensing IJRS > vol 28 n°5-6 (March 2007) . - pp 963 - 984[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 080-07031 RAB Revue Centre de documentation En réserve L003 Disponible Feature extractions for small sample size classification problem / B.C. Kuo in IEEE Transactions on geoscience and remote sensing, vol 45 n° 3 (March 2007)
[article]
Titre : Feature extractions for small sample size classification problem Type de document : Article/Communication Auteurs : B.C. Kuo, Auteur ; K.Y. Chang, Auteur Année de publication : 2007 Article en page(s) : pp 756 - 764 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] algorithme génétique
[Termes IGN] classification dirigée
[Termes IGN] décomposition du pixel
[Termes IGN] détection de contours
[Termes IGN] reconnaissance de formes
[Termes IGN] valeur propreRésumé : (Auteur) Much research has shown that the definitions of within-class and between-class scatter matrices and regularization technique are the key components to design a feature extraction for small sample size problems. In this paper, we illustrate the importance of another key component, eigenvalue decomposition method, and a new regularization technique was proposed. In the hyperspectral image experiment, the effects of these three components of feature extraction are explored on ill-posed and poorly posed conditions. The experimental results show that different regularization methods need to cooperate with different eigenvalue decomposition methods to reach the best performance, the proposed regularization method, regularized feature extraction (RFE) outperform others, and the best feature extraction for a small sample size classification problem is RFE with nonparametric weighted scatter matrices. Numéro de notice : A2007-088 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2006.885074 En ligne : https://doi.org/10.1109/TGRS.2006.885074 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28453
in IEEE Transactions on geoscience and remote sensing > vol 45 n° 3 (March 2007) . - pp 756 - 764[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-07031 RAB Revue Centre de documentation En réserve L003 Disponible Oil spill detection in Radarsat and Envisat SAR images / A.H. Solberg in IEEE Transactions on geoscience and remote sensing, vol 45 n° 3 (March 2007)
[article]
Titre : Oil spill detection in Radarsat and Envisat SAR images Type de document : Article/Communication Auteurs : A.H. Solberg, Auteur ; C. Brekke, Auteur ; P.O. Husoy, Auteur Année de publication : 2007 Article en page(s) : pp 746 - 755 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] classification dirigée
[Termes IGN] détection automatique
[Termes IGN] détection de contours
[Termes IGN] hydrocarbure
[Termes IGN] image Envisat-ASAR
[Termes IGN] image radar
[Termes IGN] image Radarsat
[Termes IGN] marée noire
[Termes IGN] pollution des mersRésumé : (Auteur) We present algorithms for automatic detection of oil spills in synthetic aperture radar (SAR) images. The algorithms consist of three main parts, namely: 1) detection of dark spots; 2) feature extraction from the dark spot candidates; and 3) classification of dark spots as oil spills or look-alikes. The algorithms have been trained on a large number of Radarsat and Envisat Advanced Synthetic Aperture Radar (ASAR) images. The performance of the algorithm is compared to manual and semiautomatic approaches in a benchmark study using 59 Radarsat and Envisat images. The algorithms can be considered to be a good alternative to manual inspection when large ocean areas are to be inspected. Copyright IEEE Numéro de notice : A2007-087 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2006.887019 En ligne : https://doi.org/10.1109/TGRS.2006.887019 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28452
in IEEE Transactions on geoscience and remote sensing > vol 45 n° 3 (March 2007) . - pp 746 - 755[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-07031 RAB Revue Centre de documentation En réserve L003 Disponible Terrestrial and submerged aquatic vegetation mapping in Fire Island national seashore using high spatial resolution remote sensing data / Y. Wang in Marine geodesy, vol 30 n° 1-2 (March - June 2007)
[article]
Titre : Terrestrial and submerged aquatic vegetation mapping in Fire Island national seashore using high spatial resolution remote sensing data Type de document : Article/Communication Auteurs : Y. Wang, Auteur ; M. Traber, Auteur ; B. Milstead, Auteur ; S. Stevens, Auteur Année de publication : 2007 Article en page(s) : pp 77 - 95 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] carte de la végétation
[Termes IGN] classification dirigée
[Termes IGN] données bathymétriques
[Termes IGN] ERDAS Imagine
[Termes IGN] extraction automatique
[Termes IGN] herbier marin
[Termes IGN] image à très haute résolution
[Termes IGN] image Quickbird
[Termes IGN] image vidéo
[Termes IGN] littoral
[Termes IGN] parc naturel national
[Termes IGN] photographie sous-marine
[Termes IGN] plante aquatique d'eau salée
[Termes IGN] plante halophile
[Termes IGN] précision de la classification
[Termes IGN] Rhode Island (Etats-Unis)Résumé : (Auteur) The vegetation communities and spatial patterns on the Fire Island National Seashore are dynamic as the result of interactions with driving forces such as sand deposition, storm-driven over wash, salt spray, surface water, as well as with human disturbances. We used high spatial resolution QuickBird-2 satellite remote sensing data to map both terrestrial and submerged aquatic vegetation communities of the National Seashore. We adopted a stratified classification and unsupervised classification approach for mapping terrestrial vegetation types. Our classification scheme included detailed terrestrial vegetation types identified by previous vegetation mapping efforts of the National Park Service and three generalized categories of high-density seagrass, low-density seagrass coverages, and unvegetated bottom to map the submerged aquatic vegetation habitats. We used underwater videography, GPS-guided field reference photography, and bathymetric data to support remote sensing image classification and information extraction. This study achieved approximately 82% and 75% overall classification accuracy for the terrestrial and submerged aquatic vegetations, respectively, and provided an updated vegetation inventory and change analysis for the Northeast Coastal and Barrier Network of the National Park Service. Copyright Taylor & Francis Numéro de notice : A2007-436 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1080/01490410701296226 En ligne : https://doi.org/10.1080/01490410701296226 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28799
in Marine geodesy > vol 30 n° 1-2 (March - June 2007) . - pp 77 - 95[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 230-07011 RAB Revue Centre de documentation En réserve L003 Disponible Generation of geometrically and radiometrically terrain corrected SAR image products / A. Loew in Remote sensing of environment, vol 106 n° 3 (15/02/2007)PermalinkExtraction of spectral channels from hyperspectral images for classification purposes / S.B. Serpico in IEEE Transactions on geoscience and remote sensing, vol 45 n° 2 (February 2007)PermalinkAssessing the effect of attribute uncertainty on the robustness of choropleth map classification / N. Xiao in International journal of geographical information science IJGIS, vol 21 n° 1-2 (january 2007)PermalinkAuto-qualification de données géographiques 3D par appariement multi-image et classification supervisée / Laurence Boudet (2007)PermalinkDétection des zones débroussaillées dans les images simulées ORFEO / Marie-Cécile Lyx (2007)PermalinkEvolution des habitats dans les montagnes d'Araucania / Rémi Pas (2007)PermalinkSeparating the weeds from the trees / M. Norris-Rogers in GIM international, vol 21 n° 1 (January 2007)PermalinkApport des données Spot et Landsat au suivi des inondations dans l'estuaire du fleuve Sénégal / A.M. Dia in Photo interprétation, vol 42 n° 4 (Décembre 2006)PermalinkComparison and integration of radar and optical data for land use / cover mapping / Nathaniel D. Herold in Geocarto international, vol 21 n° 4 (December 2006 - February 2007)PermalinkMapping salt-marsh vegetation by multispectral and hyperspectral remote sensing / E. Belluco in Remote sensing of environment, vol 105 n° 1 (15/11/2006)Permalink