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Titre : Very High Resolution (VHR) satellite imagery : processing and applications Type de document : Monographie Auteurs : Francisco Eugenio, Éditeur scientifique ; Javier Marcello, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2019 Importance : 262 p. ISBN/ISSN/EAN : 978-3-03921-757-1 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse texturale
[Termes IGN] détection du bâti
[Termes IGN] forêt alpestre
[Termes IGN] image à très haute résolution
[Termes IGN] image Pléiades
[Termes IGN] image Quickbird
[Termes IGN] image Worldview
[Termes IGN] risque naturel
[Termes IGN] traitement d'imageRésumé : (Editeur) Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing. Numéro de notice : 26310 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Numéro de périodique DOI : 10.3390/books978-3-03921-757-1 Date de publication en ligne : 09/12/2019 En ligne : https://doi.org/10.3390/books978-3-03921-757-1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95071 Automatic 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)
[article]
Titre : Automatic building rooftop extraction from aerial images via hierarchical RGB-D priors Type de document : Article/Communication Auteurs : Shibiao Xu, Auteur ; Xingjia Pan, Auteur ; Er Li, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 7369 - 7387 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] champ aléatoire conditionnel
[Termes IGN] détection du bâti
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image à haute résolution
[Termes IGN] image captée par drone
[Termes IGN] image RVB
[Termes IGN] itération
[Termes IGN] scène urbaine
[Termes IGN] segmentation d'image
[Termes IGN] segmentation hiérarchique
[Termes IGN] toit
[Termes IGN] zone saillante 3DRésumé : (auteur) Accurate building rooftop extraction from high-resolution aerial images is of crucial importance in a wide range of applications. Owing to the varying appearance and large-scale range of scene objects, especially for building rooftops in different scales and heights, single-scale or individual prior-based extraction technique is insufficient in pursuing efficient, generic, and accurate extraction results. The trend toward integrating multiscale or several cue techniques appears to be the best way; thus, such integration is the focus of this paper. We first propose a novel salient rooftop detector integrating four correlative RGB-D priors (depth cue, uniqueness prior, shape prior, and transition surface prior) for improved rooftop extraction to address the preceding complex issues mentioned. Then, these correlative cues are computed from image layers created by our multilevel segmentation and further fused into the state-of-the-art high-order conditional random field (CRF) framework to locate the rooftop. Finally, an iterative optimization strategy is applied for high-quality solving, which can robustly handle varying appearance of building rooftops. Performance evaluations in the SZTAKI-INRIA benchmark data sets show that our method outperforms the traditional color-based algorithm and the original high-order CRF algorithm and its variants. The proposed algorithm is also evaluated and found to produce consistently satisfactory results for various large-scale, real-world data sets. Numéro de notice : A2018-558 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2018.2850972 Date de publication en ligne : 26/07/2018 En ligne : http://dx.doi.org/10.1109/TGRS.2018.2850972 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91664
in IEEE Transactions on geoscience and remote sensing > vol 56 n° 12 (December 2018) . - pp 7369 - 7387[article]A greyscale voxel model for airborne lidar data applied to building detection / Liying Wang in Photogrammetric record, vol 33 n° 164 (December 2018)
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Titre : A greyscale voxel model for airborne lidar data applied to building detection Type de document : Article/Communication Auteurs : Liying Wang, Auteur ; Yuanding Zhao, Auteur ; Yu Li, Auteur Année de publication : 2018 Article en page(s) : pp 470 - 490 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] détection du bâti
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] niveau de gris (image)
[Termes IGN] semis de points
[Termes IGN] toit
[Termes IGN] voxelRésumé : (Auteur) The existing binary voxel model algorithm for 3D building detection (3BD) from airborne lidar cannot distinguish between connected buildings and non‐buildings. As a result, a greyscale voxel structure model, using the discretised mean intensity of lidar points, is presented to support subsequent building detection in areas where buildings are adjacent to non‐buildings but with different greyscales. The resulting 3BD algorithm first detects a building roof by selecting voxels characterised by a jump in elevation as seeds, labelling them and their 3D connected regions as rooftop voxels. Then voxels which fall into buffers and possess similar greyscales to that of the corresponding building outline are assigned as building façades. The results for detected buildings are evaluated using lidar data with different densities and demonstrate a high rate of success. Numéro de notice : A2018-622 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/phor.12266 Date de publication en ligne : 10/01/2019 En ligne : https://doi.org/10.1111/phor.12266 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92866
in Photogrammetric record > vol 33 n° 164 (December 2018) . - pp 470 - 490[article]Novel 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)
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Titre : Novel fusion approach on automatic object extraction from spatial data: case study Worldview-2 and TOPO5000 Type de document : Article/Communication Auteurs : Umut Gunes Sefercik, Auteur ; Serkan Karakis, Auteur ; Can Atalay, Auteur ; Ibrahim Yigit, Auteur ; Umit Gokmen, Auteur Année de publication : 2018 Article en page(s) : pp 1139 - 1154 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] détection d'objet
[Termes IGN] détection du bâti
[Termes IGN] extraction automatique
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] extraction du réseau routier
[Termes IGN] filtre de Wallis
[Termes IGN] image numérique
[Termes IGN] image Worldview
[Termes IGN] modèle numérique de surface
[Termes IGN] TurquieRésumé : (auteur) The automatic extraction of information content from remotely sensed data is always challenging. We suggest a novel fusion approach to improve the extraction of this information from mono-satellite images. A Worldview-2 (WV-2) pan-sharpened image and a 1/5000-scaled topographic vector map (TOPO5000) were used as the sample data. Firstly, the buildings and roads were manually extracted from WV-2 to point out the maximum extractable information content. Subsequently, object-based automatic extractions were performed. After achieving two-dimensional results, a normalized digital surface model (nDSM) was generated from the underlying digital aerial photos of TOPO5000, and the automatic extraction was repeated by fusion with the nDSM to include individual object heights as an additional band for classification. The contribution was tested by precision, completeness and overall quality. Novel fusion technique increased the success of automatic extraction by 7% for the number of buildings and by 23% for the length of roads. Numéro de notice : A2019-047 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2017.1353646 Date de publication en ligne : 27/07/2017 En ligne : https://doi.org/10.1080/10106049.2017.1353646 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92068
in Geocarto international > vol 33 n° 10 (October 2018) . - pp 1139 - 1154[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2018041 RAB Revue Centre de documentation En réserve L003 Disponible Extraction of building roof planes with stratified random sample consensus / André C. Carrilho in Photogrammetric record, vol 33 n° 163 (September 2018)
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Titre : Extraction of building roof planes with stratified random sample consensus Type de document : Article/Communication Auteurs : André C. Carrilho, Auteur ; Mauricio Galo, Auteur Année de publication : 2018 Article en page(s) : pp 363 - 380 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] détection du bâti
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] morphologie mathématique
[Termes IGN] Ransac (algorithme)
[Termes IGN] semis de points
[Termes IGN] toit
[Termes IGN] varianceRésumé : (Auteur) This paper describes a consensus‐set estimation for building roof‐plane detection using a stratified random sample consensus (sRANSAC) algorithm applied to point clouds acquired by laser scanning systems. The main idea is to use one initial classification to generate consensus‐set candidates to optimise the sampling mechanism compared to the original RANSAC. The initial classification is performed using mathematical morphology to filter ground returns and estimate local variance information to detect potential planar regions. Thus, the algorithm can prioritise points within planar segments and the number of iterations can be estimated dynamically from available data. The results based on experiments using five different lidar datasets indicate that the proposed method reduces the number of computations for building roof‐plane detection and also improves accuracy compared to RANSAC. Numéro de notice : A2018-620 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/phor.12254 Date de publication en ligne : 21/09/2018 En ligne : https://doi.org/10.1111/phor.12254 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92864
in Photogrammetric record > vol 33 n° 163 (September 2018) . - pp 363 - 380[article]Fusion 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