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Urban object reconstruction using airborne laser elevation image and aerial image / K. Fujii in IEEE Transactions on geoscience and remote sensing, vol 40 n° 10 (October 2002)
[article]
Titre : Urban object reconstruction using airborne laser elevation image and aerial image Type de document : Article/Communication Auteurs : K. Fujii, Auteur ; T. Arikawa, Auteur Année de publication : 2002 Article en page(s) : pp 2234 - 2240 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] classification dirigée
[Termes IGN] détection du bâti
[Termes IGN] image aérienne
[Termes IGN] modélisation 3D
[Termes IGN] reconstruction 3D du bâti
[Termes IGN] superposition d'images
[Termes IGN] télémétrie laser aéroporté
[Termes IGN] texture d'image
[Termes IGN] transformation de HoughRésumé : (Auteur) Creating three dimensional (3D) models of a real urban objects is an important goal in a wide variety of applications. This paper describes a method that utilizes airborne laser elevation images and aerial images for the 3-D reconstruction of urban objects. Our modeling approach uses the vertical geometric pattern analysis of elevation images. These pattern correspond to object contours and, thus enable the extraction of the object. In addition, to provide realistic textured details, textures are cut from aerial images and mapped onto 3-D models. Our textures-mapping approach can avoid geometry mismatching and enable the automatic registration to determine the most reliable correspondence between projected outlines of 3-D models and contours of real objects shown in aerial images. Edge pairs, which are matched with projected outlines, are detected from aerial images. In order to minimize mismatching, we apply the voting technique based on the generalized Hough transform. Experimental results show that 3-D reconstruction of urban objects generally successful. Numéro de notice : A2002-319 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2002.802460 En ligne : https://doi.org/10.1109/TGRS.2002.802460 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22230
in IEEE Transactions on geoscience and remote sensing > vol 40 n° 10 (October 2002) . - pp 2234 - 2240[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-02101 RAB Revue Centre de documentation En réserve L003 Disponible A comparison of fuzzy vs. augmented-ISODATA classification algorithms for cloud-shadow discrimination from Landsat images / A.M. Melesse in Photogrammetric Engineering & Remote Sensing, PERS, vol 68 n° 9 (September 2002)
[article]
Titre : A comparison of fuzzy vs. augmented-ISODATA classification algorithms for cloud-shadow discrimination from Landsat images Type de document : Article/Communication Auteurs : A.M. Melesse, Auteur ; J.D. Jordan, Auteur Année de publication : 2002 Article en page(s) : pp 905 - 911 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] bande visible
[Termes IGN] classification dirigée
[Termes IGN] classification floue
[Termes IGN] classification ISODATA
[Termes IGN] image Landsat-TM
[Termes IGN] nuage
[Termes IGN] ombreRésumé : (Auteur) Satellite images are the most important source of landcover data over a large range of temporal and spatial scales. However, the complete realization of satellite imagery as a source of landcover information is limited by the presence of contaminants such as cloud and associated shadows in the image. These contaminants are not adequately handled with conventional image classification techniques such as the unsupervised maximumlikelihood technique. This study comprises a comparison of two classification algorithms, the fuzzy technique and an augmented form of the Iterative SelfOrganizing Data Analysis (ISODATA) technique, which were used to discriminate lowaltitude clouds and their shadows on a Landsat Thematic Mapper (TM) image of the Econlockhatchee River basin (Econ), in central Florida. Preliminary conventional unsupervised maxim umlikelihood classification of the image resulted in clouds being mixed with builtups and shadows being mixed with water bodies. Regions containing these two kinds of mixed categories were first masked, then fuzzy and augmented ISODATA classifications were performed on them. The ISODATA classification algorithm was run on the TM visible/shortwave bands and augmented with scatter diagrams of surface temperature versus several vegetation indices; the fuzzy algorithm was run on TM bands 1 through 5 and band 7. An accuracy assessment of the techniques was carried out using 40 randomly selected points within the image. Results of the classifications showed that both algorithms successfully discriminated clouds from other bright features, and shadows from other dark features, with an overall accuracy of greater than 80 percent. Numéro de notice : A2002-182 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans En ligne : https://www.asprs.org/wp-content/uploads/pers/2002journal/september/2002_sep_905 [...] Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22097
in Photogrammetric Engineering & Remote Sensing, PERS > vol 68 n° 9 (September 2002) . - pp 905 - 911[article]Land cover classification models using Shuttle Imaging Radar (SIR-C) data: a case study in New Hampshire, USA / R. Narayanan in Geocarto international, vol 17 n° 3 (September - November 2002)
[article]
Titre : Land cover classification models using Shuttle Imaging Radar (SIR-C) data: a case study in New Hampshire, USA Type de document : Article/Communication Auteurs : R. Narayanan, Auteur ; Jing Zhang, Auteur Année de publication : 2002 Article en page(s) : pp 57 - 65 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] classification dirigée
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] classification par réseau neuronal
[Termes IGN] covariance
[Termes IGN] fréquence
[Termes IGN] image radar
[Termes IGN] image SIR-C
[Termes IGN] New Hampshire (Etats-Unis)
[Termes IGN] occupation du sol
[Termes IGN] polarisation
[Termes IGN] précision de la classification
[Termes IGN] réalité de terrain
[Termes IGN] varianceRésumé : (Auteur) Spaceborne synthetic aperture radar (SAR) systems have the ability to provide high resolution information on land cover characteristics under adverse conditions such as darkness or cloud cover. The use of multiple frequencies and multiple polarizations yields better classification accuracies. The results of various land cover classification algorithms using Shuttle Imaging Radar (SIR-C) SAR data as applied to a site in Suncook, New Hampshire, are described in this paper. Three classification models were developed and tested: minimum distance classification, maximum a posteriori probability classification, and neural network classification. Using the available ground truth information, land cover was classified into five distinct regions: water, swamp, sand, trees, and grass. All three methods were applied to the same site and results compared. The maximum a posteriori probability approach yielded the highest overall classification accuracy on a pixelbypixel basis. Although the minimum distance approach was simpler than the maximum a posteriori approach, its performance was not as good as the latter since it did not use the covariance information between the data channels. The neural network approach performed well and its results were comparable to the maximum a posteriori approach when the variance of the data was small; however, its performance degraded rapidly when the variance of the data was high. Numéro de notice : A2002-286 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106040208542245 En ligne : https://doi.org/10.1080/10106040208542245 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22197
in Geocarto international > vol 17 n° 3 (September - November 2002) . - pp 57 - 65[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-02031 RAB Revue Centre de documentation En réserve L003 Disponible Evaluation of SAR-optical imagery synthesis techniques in a complex coastal ecosystem / F.M. Henderson in Photogrammetric Engineering & Remote Sensing, PERS, vol 68 n° 8 (August 2002)
[article]
Titre : Evaluation of SAR-optical imagery synthesis techniques in a complex coastal ecosystem Type de document : Article/Communication Auteurs : F.M. Henderson, Auteur ; R. Chasan, Auteur ; J. Portolese, Auteur ; T. Hart, Auteur Année de publication : 2002 Article en page(s) : pp 839 - 846 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] classification ascendante hiérarchique
[Termes IGN] classification dirigée
[Termes IGN] écosystème
[Termes IGN] Etats-Unis
[Termes IGN] image optique
[Termes IGN] image radar
[Termes IGN] littoral
[Termes IGN] précision
[Termes IGN] radar à antenne synthétique
[Termes IGN] Thematic MapperRésumé : (Auteur) Coastal areas comprise some of the world's most important and sensitive ecosystems. Although optical remote sensing systems have demonstrated an ability to map land cover in many coastal environments, spectral confusion has also been reported. The parameters of SAR imagery suggest that combinations of SAR and optical data may improve land-cover classification accuracy. Seven satellite SAR data sets were merged with TM data using four techniques. These were tested by classifying 11 upland and wetland land covers in a rapidly urbanizing coastal area of the northeast United States. Not all SABITM combinations bettered the accuracy obtained using TM data alone. In general, simple techniques improved accuracy more than did complex image merge methods. Although one SAR image proved superior overall, improvement in detection accuracy varied among individual land-cover categories and SAR data. The results point to the possible benefits of hierarchical-layered classifications. Numéro de notice : A2002-164 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans En ligne : https://www.asprs.org/wp-content/uploads/pers/2002journal/august/2002_aug_839-84 [...] Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22079
in Photogrammetric Engineering & Remote Sensing, PERS > vol 68 n° 8 (August 2002) . - pp 839 - 846[article]An experimental study on content-based image classication for image databases / R.D. Holowczak in IEEE Transactions on geoscience and remote sensing, vol 40 n° 6 (June 2002)
[article]
Titre : An experimental study on content-based image classication for image databases Type de document : Article/Communication Auteurs : R.D. Holowczak, Auteur ; F.J. Artigas, Auteur ; S.A. Chunfang, Auteur ; J.S. Cho, Auteur ; H.S. Stone, Auteur Année de publication : 2002 Article en page(s) : pp 1338 - 1347 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] base de données d'images
[Termes IGN] classification dirigée
[Termes IGN] image NOAA-AVHRR
[Termes IGN] nébulosité
[Termes IGN] reconnaissance automatique
[Termes IGN] zone d'intérêtRésumé : (Auteur) Current art uses metadata associated with satellite images to facilitate their retrieval from image repositories. Typical metadata are geographic location, time, and data type. Because the metadata do not indicate which regions within an image are obscured by clouds, retrieval with such metadata may produce an image within which the region of interest (ROI) for the user is not visible. We report a system that can automatically determine whether an ROI is visible in the image, and can incorporate this into the metadata for individual images to enhance searching capability. The goal is to annotate each image with metadata regarding a number of ROIs. An experiment with the system annotated 236 advanced very high resolution radiometer (AVHRR) images of the North Atlantic from a flvemonth viewing period with descriptors that expressed the visibility of an ROI centered on Long Island, NY. For ground truth, we used the classifications of three human subjects to determine visibility of the same region of interest, and labeled the ROI with the majority decision of the three subjects. Partial cloud cover made the human determination subjective, and resulted in disagreements among the subjects. Using randomly selected training subsets of the images, we found the two images whose regions were most like those in images for which the Long Island region was visible. For training subsets, the descriptors derived from the two best images produced average recall and precision retrieval results jointly in the 75% to 80% region. Descriptors derived from those same two images for the test subsets also produced average recall and precision results that jointly fell in the 75% to 80% region. Numéro de notice : A2002-191 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2002.800751 Date de publication en ligne : 02/08/2002 En ligne : https://doi.org/10.1109/TGRS.2002.800751 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22106
in IEEE Transactions on geoscience and remote sensing > vol 40 n° 6 (June 2002) . - pp 1338 - 1347[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-02061 RAB Revue Centre de documentation En réserve L003 Disponible 065-02062 RAB Revue Centre de documentation En réserve L003 Disponible Cognitive geometry for cartography / J. Comenetz in Cartographic journal (the), vol 39 n° 1 (June 2002)PermalinkFusion radar and optical data for land cover mapping / Nathaniel D. Herold in Geocarto international, vol 17 n° 2 (June - August 2002)PermalinkThe UK land cover map 2000: construction of a parcel-based vector map from satellite images / R.M. Fuller in Cartographic journal (the), vol 39 n° 1 (June 2002)PermalinkImagerie spatiale et aménagements forestiers au Gabon / Marcellin Nziengui in Bulletin [Société Française de Photogrammétrie et Télédétection], n° 167 (Avril 2002)PermalinkMultiple classifier systems for supervised remote sensing image classification based on dynamic classifier selection / P.C. Smits in IEEE Transactions on geoscience and remote sensing, vol 40 n° 4 (April 2002)PermalinkComparison 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)PermalinkA 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)PermalinkFuzzy rule-based classification of remotely sensed imagery / A. Bardossy in IEEE Transactions on geoscience and remote sensing, vol 40 n° 2 (February 2002)PermalinkAnalyse 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)PermalinkApport des SIG et de la télédétection à la détermination d'unités dynamiques des paysages / S. Bancarel (2002)Permalink