Descripteur
Termes IGN > sciences naturelles > physique > traitement d'image > analyse d'image numérique > extraction de traits caractéristiques > détection de contours > détection du bâti
détection du bâtiSynonyme(s)extraction du bâtiVoir aussi |
Documents disponibles dans cette catégorie (210)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Titre : Spatial variability in environmental science : patterns, processes, and analyses Type de document : Monographie Auteurs : John P. Tiefenbacher, Éditeur scientifique ; Davod Poreh, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2020 ISBN/ISSN/EAN : 978-1-83962-461-2 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] aménagement paysager
[Termes IGN] aquaculture
[Termes IGN] azote
[Termes IGN] détection du bâti
[Termes IGN] données environnementales
[Termes IGN] données GNSS
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] données spatiotemporelles
[Termes IGN] écologie forestière
[Termes IGN] forêt
[Termes IGN] ilot thermique urbain
[Termes IGN] littoral
[Termes IGN] photogrammétrie numérique
[Termes IGN] photographie aérienne
[Termes IGN] pollution atmosphérique
[Termes IGN] soufre
[Termes IGN] surveillance météorologique
[Termes IGN] ventRésumé : (Editeur) This book includes eight studies that examine the issue of spatial variability in four areas of the environmental sciences – atmospheric science, geological science, biological science, and landscape science. The topics range from monitoring of wind, the urban heat island, and atmospheric pollution, to coastal geomorphology, landscape planning and forest ecology, the problem of introduced species to regional ecologies, and a technique to improve the identification of human constructions in semi-natural landscapes. A small volume can only offer a small glimpse at the activities of scientists and insights into environmental science, but the array of papers herein offers a unique view of the current scholarship. Note de contenu :
1. Coherent Doppler Lidar for Wind Sensing
2. Low-Key Stationary and Mobile Tools for Probing the Atmospheric UHI Effect
3. Mapping and Estimation of Nitrogen and Sulfur Atmospheric Deposition Fluxes in Central Region of the Mexican Bajio
4. Monitoring Storm Impacts on Sandy Coastlines with UAVs
5. Recent Advances in Coastal Survey Techniques: From GNSS to LiDAR and Digital Photogrammetry - Examples on the Northern Coast of France
6. Spatial and Temporal Variability Regarding Forest: From Tree to the Landscape
7. Ecological and Social Impacts of Aquacultural Introduction to Philippines Waters of Pacific Whiteleg Shrimp Penaeus vannamei
8. High-Resolution Object-Based Building Extraction Using PCA of LiDAR nDSM and Aerial PhotosNuméro de notice : 26692 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/URBANISME Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.87655 Date de publication en ligne : 21/10/2020 En ligne : https://doi.org/10.5772/intechopen.87655 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99032 A versatile and efficient data fusion methodology for heterogeneous airborne LiDAR and optical imagery data acquired under unconstrained conditions / Thanh Huy Nguyen (2020)
Titre : A versatile and efficient data fusion methodology for heterogeneous airborne LiDAR and optical imagery data acquired under unconstrained conditions Type de document : Thèse/HDR Auteurs : Thanh Huy Nguyen, Auteur ; Jean-Marc Le Caillec, Directeur de thèse ; Sylvie Daniel, Directeur de thèse Editeur : Institut Mines-Télécom Atlantique IMT Atlantique Année de publication : 2020 Autre Editeur : Québec : Université Laval Importance : 173 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse de Doctorat de l'Ecole Nationale Supérieure des Mines-Telecom Atlantique Bretagne Pays de la Loire-IMT Atlantique, Spécialité : Signal, Image, VisionLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] détection du bâti
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] fusion de données
[Termes IGN] image optique
[Termes IGN] recalage de données localisées
[Termes IGN] reconstruction 3D
[Termes IGN] scène urbaine
[Termes IGN] semis de pointsIndex. décimale : THESE Thèses et HDR Résumé : (auteur) The necessity and importance of representing a scene in 3-D have been exemplified through numerous remote sensing applications, such as urban planning, disaster management, etc. In these applications, LiDAR and optical imagery data have been used extensively. A complementarity existing between airborne LiDAR and aerial/satellite optical imagery datasets motivates the fusion between them, allowing to represent the observed scenes in 3-D with a better precision and completeness. In recent years, automatic building footprint extraction in urban and residential scenes has become a subject of growing interest among the field of 3-D scene representation and reconstruction. With the rising availability of massive amount of data captured by different LiDAR and imagery sensors onboard airborne and spaceborne platforms, new opportunities arise to perform this task on a large scale. However, existing fusion methods generally consider either hybrid acquisition systems consisting of LiDAR and optical cameras rigidly fixed, or datasets acquired from the same platform at identical or very close dates, and having the same spatial resolution. They do not intend to cope with datasets collected from different platforms with different acquisition configuration at different moments, having different spatial resolutions and levels of detail. Such a context is referred to as unconstrained acquisition context. Furthermore, extracting buildings on a large scale is a complex task. Existing methods reported over the years have achieved relatively significant results by assuming building shapes, enforcing geometrical constraints, or limiting on specific urban areas. Such assumptions are no longer applicable when dealing with large-scale datasets. This research work is devoted to the development of a versatile coarse-to-fine registration method between airborne LiDAR and aerial/satellite optical imagery datasets collected in an unsconstrained acquisition context. It aims at overcoming the challenges associated with this context such as the spatial shift between the datasets, the differences of spatial resolution and level of detail, etc. In addition, this research work elaborates an efficient building footprint extraction method, providing a high accuracy level while being an unsupervised method dedicated to largescale applications. The proposed method, called Super-Resolution-based Snake Model (SRSM), consists in an adaptation of snake models—a conventional image segmentation technique—to operate on high-resolution LiDAR-based elevation images generated by a super-resolution process. It pertains the unconstrained data acquisition context, serving as a prime application example. Relevant results have been achieved when rigorously assessing the proposed methods, namely a highly desirable accuracy level compared to existing methods. Note de contenu : Introduction
1- State of the art
2- Coarse-to-fine Registration of Airborne LiDAR and Optical Imagery Data on Urban Scenes
3- Building Extraction Based on the Fusion of Airborne LiDAR and Optical Imagery Data
4- Conclusions and PerspectivesNuméro de notice : 28327 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Sciences Géomatiques : Mines-Télécom Atlantique : 2020 Organisme de stage : Lab-STICC DOI : sans En ligne : https://tel.archives-ouvertes.fr/tel-03123328/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98401 Residences information extraction from Landsat imagery using the multi-parameter decision tree method / Yujie Yang in Geocarto international, vol 34 n° 14 ([30/10/2019])
[article]
Titre : Residences information extraction from Landsat imagery using the multi-parameter decision tree method Type de document : Article/Communication Auteurs : Yujie Yang, Auteur ; Shijie Wang, Auteur ; Xiaoyong Bai, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 1621 - 1633 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] albedo
[Termes IGN] analyse spectrale
[Termes IGN] classification par arbre de décision
[Termes IGN] détection de changement
[Termes IGN] détection du bâti
[Termes IGN] eau
[Termes IGN] image Landsat-OLI
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] occupation du sol
[Termes IGN] ombre
[Termes IGN] série temporelle
[Termes IGN] seuillage d'imageRésumé : (auteur) The rapid and accurate grasp of changes in residences is crucial for urban planning and urbanisation. However, the traditional methods for extracting residences exists several problems, which lead to inaccurate extraction results. In this study, the Landsat image is used to establish a new method for extracting the residences quickly and accurately. The specific steps are as follows: (1) We calculate surface albedo to exclude the interference of waters and shadows; (2) Using single-band threshold method, we eliminate the interference of shadows; (3) Normalized Difference Vegetation Index is calculated to exclude the effects of vegetation; (4) Roads are removed by calculating the shape index. Verification shows that the accuracy of this extraction method is 92.81%, which is more accurate than the traditional methods and solves the problems existed in the traditional methods. This novel method is a new reference for other land cover research on the technical aspect. Numéro de notice : A2019-528 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1494760 Date de publication en ligne : 07/09/2018 En ligne : https://doi.org/10.1080/10106049.2018.1494760 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94106
in Geocarto international > vol 34 n° 14 [30/10/2019] . - pp 1621 - 1633[article]Accurate detection of built-up areas from high-resolution remote sensing imagery using a fully convolutional network / Yihua Tan in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 10 (October 2019)
[article]
Titre : Accurate detection of built-up areas from high-resolution remote sensing imagery using a fully convolutional network Type de document : Article/Communication Auteurs : Yihua Tan, Auteur ; Shengzhou Xiong, Auteur ; Zhi Li, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 737 - 752 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection du bâti
[Termes IGN] extraction automatique
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image à haute résolution
[Termes IGN] image Worldview
[Termes IGN] segmentation sémantiqueRésumé : (Auteur) The analysis of built-up areas has always been a popular research topic for remote sensing applications. However, automatic extraction of built-up areas from a wide range of regions remains challenging. In this article, a fully convolutional network (FCN)–based strategy is proposed to address built-up area extraction. The proposed algorithm can be divided into two main steps. First, divide the remote sensing image into blocks and extract their deep features by a lightweight multi-branch convolutional neural network (LMB-CNN). Second, rearrange the deep features into feature maps that are fed into a well-designed FCN for image segmentation. Our FCN is integrated with multi-branch blocks and outputs multi-channel segmentation masks that are utilized to balance the false alarm and missing alarm. Experiments demonstrate that the overall classification accuracy of the proposed algorithm can achieve 98.75% in the test data set and that it has a faster processing compared with the existing state-of-the-art algorithms. Numéro de notice : A2019-522 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.10.737 Date de publication en ligne : 01/10/2019 En ligne : https://doi.org/10.14358/PERS.85.10.737 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93992
in Photogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 10 (October 2019) . - pp 737 - 752[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 105-2019101 SL Revue Centre de documentation Revues en salle Disponible Delineation of vacant building land using orthophoto and lidar data object classification / Dejan Jenko in Geodetski vestnik, vol 63 n° 3 (September - November 2019)
[article]
Titre : Delineation of vacant building land using orthophoto and lidar data object classification Type de document : Article/Communication Auteurs : Dejan Jenko, Auteur ; Mojca Foški, Auteur ; Krištof Oštir, Auteur ; Žiga Kokalj, Auteur Année de publication : 2019 Article en page(s) : pp 344 - 378 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] classification orientée objet
[Termes IGN] couche thématique
[Termes IGN] détection du bâti
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] logement
[Termes IGN] orthoimage
[Termes IGN] SlovénieRésumé : (Auteur) Exact data about the location and area of vacant building land have been a major issue in several Slovene municipalities. This article deals with automatic vacant building land delineation. The presented methodology is based on the object-based classification that derives the land cover layer from orthophoto and laser scanning data. With post-processing and data cleaning in GIS, we create the vacant building land layer. The methodology was tested in study areas in the Municipality of Trebnje. The results were compared to the vacant building land layer generated by visual interpretation (manual vectorisation). We found that the presented methodology of automatic delineation of vacant buildings can speed up the processing and lower the cost of manual vectorisation and, in particular, data updating but we cannot completely replace manual work. Numéro de notice : A2019-500 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.15292/geodetski-vestnik.2019.03.344-378 En ligne : http://dx.doi.org/10.15292/geodetski-vestnik.2019.03.344-378 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93782
in Geodetski vestnik > vol 63 n° 3 (September - November 2019) . - pp 344 - 378[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 139-2019031 RAB Revue Centre de documentation En réserve L003 Disponible Automatic building extraction from high-resolution aerial images and LiDAR data using gated residual refinement network / Jianfeng Huang in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)PermalinkBuilding detection and regularisation using DSM and imagery information / Yousif A. Mousa in Photogrammetric record, vol 34 n° 165 (March 2019)PermalinkRepeated structure detection for 3D reconstruction of building façade from mobile lidar data / Yanming Chen in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 2 (February 2019)PermalinkIntegration of lidar data and GIS data for point cloud semantic enrichment at the point level / Harith Aljumaily in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 1 (January 2019)PermalinkPermalinkPermalinkAutomatic building rooftop extraction from aerial images via hierarchical RGB-D priors / Shibiao Xu in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)PermalinkA greyscale voxel model for airborne lidar data applied to building detection / Liying Wang in Photogrammetric record, vol 33 n° 164 (December 2018)PermalinkNovel fusion approach on automatic object extraction from spatial data: case study Worldview-2 and TOPO5000 / Umut Gunes Sefercik in Geocarto international, vol 33 n° 10 (October 2018)PermalinkExtraction of building roof planes with stratified random sample consensus / André C. Carrilho in Photogrammetric record, vol 33 n° 163 (September 2018)PermalinkFusion of images and point clouds for the semantic segmentation of large-scale 3D scenes based on deep learning / Rui Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 143 (September 2018)PermalinkFusion tardive d’images SPOT 6/7 et de données multitemporelles Sentinel-2 pour la détection de la tache urbaine / Cyril Wendl in Revue Française de Photogrammétrie et de Télédétection, n° 217-218 (juin - septembre 2018)PermalinkDecision fusion of SPOT6 and multitemporal Sentinel2 images for urban area detection / Cyril Wendl (2018)PermalinkLocalisation par l'image en milieu urbain : application à la réalité augmentée / Antoine Fond (2018)PermalinkRaffinement de la localisation d’images provenant de sites participatifs pour la mise à jour de SIG urbain / Bernard Semaan (2018)PermalinkBuilding extraction from fused LiDAR and hyperspectral data using Random Forest Algorithm / Saeid Parsian in Geomatica, vol 71 n° 4 (December 2017)PermalinkExtraction du bâti sur le territoire de la wilaya de Blida (Algérie) / Siham Bougdour in Géomatique expert, n° 119 (novembre - décembre 2017)PermalinkSimultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks / Rasha Alshehhi in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)PermalinkOn the fusion of lidar and aerial color imagery to detect urban vegetation and buildings / Madhurima Bandyopadhyay in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 2 (February 2017)PermalinkFusion of multi-temporal Sentinel-2 image series and very-high spatial resolution images for detection of urban areas / Cyril Wendl (2017)Permalink