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Correction of positional errors and geometric distortions in topographic maps and DEMs using a rigorous SAR simulation technique / H. Liu in Photogrammetric Engineering & Remote Sensing, PERS, vol 70 n° 9 (September 2004)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 70 n° 9 (September 2004) . - pp 1031 - 1042 Titre : Correction of positional errors and geometric distortions in topographic maps and DEMs using a rigorous SAR simulation technique Type de document : Article/Communication Auteurs : H. Liu, Auteur ; Z. Zhao, Auteur ; K.C. Jezek, Auteur Année de publication : 2004 Article en page(s) : pp 1031 - 1042 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] Antarctique [Termes IGN] carte topographique [Termes IGN] correction géométrique [Termes IGN] erreur de positionnement [Termes IGN] image Radarsat [Termes IGN] jeu de données localisées [Termes IGN] modèle mathématique [Termes IGN] modèle numérique de surface [Termes IGN] simulation Résumé : (Auteur) In the history of surveying and mapping, a Iarge volumes of topographic maps and digital elevation models have been created at various scales throughout the world. However, positional errors and geometric distortions may exist in the topographic contour maps and their derived DEMs due to inaccurate ground control and poor navigation techniques in the early years. In this paper, we present a new technique to detect and correct positional errors and geometric distortions in topographic data based on rigorous Synthetic Aperture Radar (SAR) image simulation and mathematical modeling of SAR imaging geometry. Our method has been successfully applied to two USGS topographical data sets in Antarctica. Using Radarsat SAR imagery, positional errors of these two data sets have been reduced from 5 km to 200 m and from 200 m to 50 m, respectively. Numéro de notice : A2004-345 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.70.9.1031 En ligne : http://dx.doi.org/10.14358/PERS.70.9.1031 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26872 [article]

Automated extraction of coastline from satellite imagery by integrating canny edge detection and locally adaptative thresholding methods / H. Liu in International Journal of Remote Sensing IJRS, vol 25 n° 5 (March 2004)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 25 n° 5 (March 2004) . - pp 937 - 958 Titre : Automated extraction of coastline from satellite imagery by integrating canny edge detection and locally adaptative thresholding methods Type de document : Article/Communication Auteurs : H. Liu, Auteur ; K.C. Jezek, Auteur Année de publication : 2004 Article en page(s) : pp 937 - 958 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] courbe de Gauss [Termes IGN] détection de contours [Termes IGN] extraction automatique [Termes IGN] image optique [Termes IGN] image radar [Termes IGN] image satellite [Termes IGN] littoral [Termes IGN] segmentation d'image [Termes IGN] seuillage d'image [Termes IGN] trait de côte Résumé : (Auteur) This paper presents a comprehensive approach to effectively and accurately extract coastlines from satellite imagery. It consists of a sequence of image processing algorithms, in which the key component is image segmentation based on a locally adaptive thresholding technique. Several technical innovations have been made to improve the accuracy and efficiency for determining the land/ water boundaries. The use of the Levenberg-Marquardt method and the Canny edge detector speeds up the convergence of iterative Gaussian curve fitting process and improves the accuracy of the bimodal Gaussian parameters. The result is increased reliability of local thresholds for image segmentation. A series of further image processing steps are applied to the segmented images. Particularly, grouping and labelling contiguous image regions into individual image objects enables us to utilize heuristic human knowledge about the size and continuity of the land and ocean masses to discriminate the true coastline from other object boundaries. The final product of our processing chain is a vector-based line coverage of the coastline, which can be readily incorporated into a GIS database. Our method has been applied to both radar and optical satellite images, and the positional precision of the resulting coastline is measured at the pixel level. Numéro de notice : A2004-077 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/0143116031000139890 En ligne : https://doi.org/10.1080/0143116031000139890 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26605 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-04051	RAB	Revue	Centre de documentation	En réserve L003	Exclu du prêt