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Automated extraction of 3D vector topographic feature line from terrain point cloud / Wei Zhou in Geocarto international, vol 33 n° 10 (October 2018)
[article]
Titre : Automated extraction of 3D vector topographic feature line from terrain point cloud Type de document : Article/Communication Auteurs : Wei Zhou, Auteur ; Rencan Peng, Auteur ; Jian Dong, Auteur ; Tao Wang, Auteur Année de publication : 2018 Article en page(s) : pp 1036 - 1047 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] arbre aléatoire minimum
[Termes IGN] détection d'objet
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] ligne caractéristique
[Termes IGN] lissage de données
[Termes IGN] modèle numérique de terrain
[Termes IGN] objet géographique linéaire
[Termes IGN] repère de Laplace
[Termes IGN] segmentation en régions
[Termes IGN] semis de pointsRésumé : (auteur) This paper presents an automated topographic feature lines detection method that directly extracts 3D vector topographic feature lines from terrain point cloud. First, signed surface variation (SSV) is introduced to extract the potential feature points. Secondly, the potential feature points are segmented to different clusters by combining region growing segmentation and conditional Euclidean clustering. In order to extract feature points, the potential feature points in each cluster are iteratively thinned using a HC-Laplacian smoothing method with SSV weighted taken into account. Besides, SSV-based and elevation-based simple rules are added for accelerating this thinning process. Finally, the feature lines are obtained by constructing the minimum spanning tree of the extracted feature points. By comparing with manually digitized reference lines, the correctness and the completeness of extracted results are about 80% or even higher, which are much higher than those extracted by D8 algorithm. Numéro de notice : A2019-046 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2017.1325521 Date de publication en ligne : 18/05/2017 En ligne : https://doi.org/10.1080/10106049.2017.1325521 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92064
in Geocarto international > vol 33 n° 10 (October 2018) . - pp 1036 - 1047[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2018041 RAB Revue Centre de documentation En réserve L003 Disponible
Titre : Classification of water surfaces using airborne topographic lidar data Type de document : Article/Communication Auteurs : Julien Smeeckaert, Auteur ; Clément Mallet , Auteur ; Nicolas David , Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2013 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 40-1/W1 Conférence : ISPRS 2013, Hannover Workshop 21/05/2013 24/05/2013 Hanovre Allemagne OA ISPRS Archives Importance : pp 321 - 326 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] chaîne de traitement
[Termes IGN] classification dirigée
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] cours d'eau
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] eau de surface
[Termes IGN] interpolation
[Termes IGN] littoral
[Termes IGN] modèle numérique de terrain
[Termes IGN] rivage
[Termes IGN] segmentation en régions
[Termes IGN] semis de pointsRésumé : (auteur) Accurate Digital Terrain Models (DTM) are inevitable inputs for mapping areas subject to natural hazards. Topographic airborne laser scanning has become an established technique to characterize the Earth surface: lidar provides 3D point clouds allowing a fine reconstruction of the topography. For flood hazard modeling, the key step before terrain modeling is the discrimination of land and water surfaces within the delivered point clouds. Therefore, instantaneous shoreline, river borders, inland waters can be extracted as a basis for more reliable DTM generation. This paper presents an automatic, efficient, and versatile workflow for land/water classification of airborne topographic lidar data. For that purpose, a classification framework based on Support Vector Machines (SVM) is designed. First, a restricted set of features, based only 3D lidar point coordinates and flightline information, is defined. Then, the SVM learning step is performed on small but well-targeted areas thanks to an automatic region growing strategy. Finally, label probabilities given by the SVM are merged during a probabilistic relaxation step in order to remove pixel-wise misclassification. Results show that survey of millions of points are labelled with high accuracy (>95% in most cases for coastal areas, and >89% for rivers) and that small natural and anthropic features of interest are still well classified though we work at low point densities (0.5-4 pts/m2). Our approach is valid for coasts and rivers, and provides a strong basis for further discrimination of land-cover classes and coastal habitats. Numéro de notice : C2013-055 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprsarchives-XL-1-W1-321-2013 Date de publication en ligne : 02/05/2013 En ligne : https://doi.org/10.5194/isprsarchives-XL-1-W1-321-2013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92183 Automatic cloud detection from multi-temporal satellite images: towards the use of Pléiades time series / Nicolas Champion (2012)
contenu dans Proceedings, Commission 3, XXII ISPRS Congress, 25 August – 01 September 2012, Melbourne, Australia / M. Shortis (2012)
Titre : Automatic cloud detection from multi-temporal satellite images: towards the use of Pléiades time series Type de document : Article/Communication Auteurs : Nicolas Champion , Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2012 Collection : International Archives of Photogrammetry and Remote Sensing, ISSN 0252-8231 num. 39-B3 Conférence : ISPRS 2012, Commission 3, 22th international congress 25/08/2012 01/09/2012 Melbourne Australie OA ISPRS Archives Commission 3 Importance : pp 559 - 564 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie spatiale
[Termes IGN] détection des nuages
[Termes IGN] image multitemporelle
[Termes IGN] image Pléiades-HR
[Termes IGN] image SPOT-HRS
[Termes IGN] segmentation en régionsRésumé : (auteur) Contrary to aerial images, satellite images are often affected by the presence of clouds. Identifying and removing these clouds is one of the primary steps to perform when processing satellite images, as they may alter subsequent procedures such as atmospheric corrections, DSM production or land cover classification. The main goal of this paper is to present the cloud detection approach, developed at the French Mapping agency. Our approach is based on the availability of multi-temporal satellite images (i.e. time series that generally contain between 5 and 10 images) and is based on a region-growing procedure. Seeds (corresponding to clouds) are firstly extracted through a pixel-to-pixel comparison between the images contained in time series (the presence of a cloud is here assumed to be related to a high variation of reflectance between two images). Clouds are then delineated finely using a dedicated region-growing algorithm. The method, originally designed for panchromatic SPOT5-HRS images, is tested in this paper using time series with 9 multi-temporal satellite images. Our preliminary experiments show the good performances of our method. In a near future, the method will be applied to Pléiades images, acquired during the in-flight commissioning phase of the satellite (launched at the end of 2011). In that context, this is a particular goal of this paper to show to which extent and in which way our method can be adapted to this kind of imagery. Numéro de notice : C2012-017 Affiliation des auteurs : LASTIG MATIS (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprsarchives-XXXIX-B3-559-2012 En ligne : http://dx.doi.org/10.5194/isprsarchives-XXXIX-B3-559-2012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94281 Region based segmentation of Quickbird multispectral imagery through band ratios and fuzzy comparison / B. Wuest in ISPRS Journal of photogrammetry and remote sensing, vol 64 n° 1 (January - February 2009)
[article]
Titre : Region based segmentation of Quickbird multispectral imagery through band ratios and fuzzy comparison Type de document : Article/Communication Auteurs : B. Wuest, Auteur ; Y. Zhang, Auteur Année de publication : 2009 Article en page(s) : pp 55 - 64 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] étalonnage de capteur (imagerie)
[Termes IGN] image à très haute résolution
[Termes IGN] image multibande
[Termes IGN] image Quickbird
[Termes IGN] partition d'image
[Termes IGN] segmentation d'image
[Termes IGN] segmentation en régionsRésumé : (Auteur) The continued advancements in satellite sensor technologies have increased the number of objects that can be discriminated within satellite imagery. Effective segmentation of high resolution satellite imagery is currently a hot topic of research. Existing segmentation algorithms and applications contain many parameters and options which require the operator to select a proper set of parameters for a given data set. The setting of these parameters can be quite tedious and the same set of parameters may or may not work from one high resolution satellite image scene to the next. This paper presents a modification of a region based approach for unsupervised segmentation of high resolution satellite imagery as a solution to segmentation of land use coverage in QuickBird multispectral 2.44 m imagery. This type of segmentation is important to a variety of applications such as land use classification and urban planning. All region based segmentation approaches require a method for representing image regions/segments and judging the similarity between two given image regions/segments. In the proposed modification of this paper, region description is provided through the integration of band ratios. Region similarity measures are performed using Fuzzy Logic. The Hierarchical Split Merge Refinement (HSMR) algorithmic framework for unsupervised image segmentation is the foundation for this modification. In addition, this paper improves upon the merging and refinement processes of the HSMR algorithm. Test results demonstrate stable segmentation of land use areas across a variety of high resolution satellite images. Copyright ISPRS Numéro de notice : A2009-028 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2008.06.005 En ligne : https://doi.org/10.1016/j.isprsjprs.2008.06.005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29658
in ISPRS Journal of photogrammetry and remote sensing > vol 64 n° 1 (January - February 2009) . - pp 55 - 64[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-09011 SL Revue Centre de documentation Revues en salle Disponible Detection, characterization, and modeling vegetation in urban areas from high-resolution aerial imagery / Corina Iovan in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol 1 n° 3 (2008)
[article]
Titre : Detection, characterization, and modeling vegetation in urban areas from high-resolution aerial imagery Type de document : Article/Communication Auteurs : Corina Iovan , Auteur ; Didier Boldo , Auteur ; Matthieu Cord, Auteur Année de publication : 2008 Article en page(s) : pp 206 - 213 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image numérique
[Termes IGN] caractérisation
[Termes IGN] classification dirigée
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] détection d'arbres
[Termes IGN] image aérienne
[Termes IGN] image infrarouge couleur
[Termes IGN] modèle 3D de l'espace urbain
[Termes IGN] modèle numérique de surface
[Termes IGN] segmentation en régions
[Termes IGN] zone urbaineRésumé : (auteur) Research in the area of 3D city modelling from remote sensed data greatly developed in recent years with an emphasis on systems dealing with the detection and representation of man-made objects, such as buildings and streets. While these systems produce accurate representations of urban environments, they ignore information about the vegetation component of a city. This paper presents a complete image analysis system which, from high-resolution color infrared (CIR) digital images, and a Digital Surface Model (DSM), extracts, segments and classifies vegetation in high density urban areas, with very high reliability. The process starts with the extraction of all vegetation areas using a supervised classification system based on a Support Vector Machines (SVM) classifier. The result of this first step is further on used to separate trees from lawns using texture criteria computed on the DSM. Tree crown borders are identified through a robust region growing algorithm based on tree-shape criteria. A SVM classifier gives the species class for each tree region previously identified. This classification is used to enhance the appearance of 3D city models by a realistic representation of vegetation according to the vegetation land use, shape and tree species. Numéro de notice : A2008-657 Affiliation des auteurs : MATIS+Ext (1993-2011) Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1109/JSTARS.2008.2007514 En ligne : https://doi.org/10.1109/JSTARS.2008.2007514 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99064
in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing > vol 1 n° 3 (2008) . - pp 206 - 213[article]Documents numériques
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