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Automatic registration of optical imagery with 3D LiDAR data using statistical similarity / Ebadat Ghanbari Parmehr in ISPRS Journal of photogrammetry and remote sensing, vol 88 (February 2014)
[article]
Titre : Automatic registration of optical imagery with 3D LiDAR data using statistical similarity Type de document : Article/Communication Auteurs : Ebadat Ghanbari Parmehr, Auteur ; Clive Simpson Fraser, Auteur ; Chunsun Zhang, Auteur ; Joseph Leach, Auteur Année de publication : 2014 Article en page(s) : pp 28 - 40 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] appariement d'images
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] image optique
[Termes IGN] semis de points
[Termes IGN] similitude
[Termes IGN] superposition d'images
[Termes IGN] superposition de donnéesRésumé : (Auteur) The development of robust and accurate methods for automatic registration of optical imagery and 3D LiDAR data continues to be a challenge for a variety of applications in photogrammetry, computer vision and remote sensing. This paper proposes a new approach for the registration of optical imagery with LiDAR data based on the theory of Mutual Information (MI), which exploits the statistical dependency between same- and multi-modal datasets to achieve accurate registration. The MI-based similarity measures quantify dependencies between aerial imagery, and both LiDAR intensity data and 3D point cloud data. The needs for specific physical feature correspondences, which are not always attainable in the registration of imagery with 3D point clouds, are avoided. Current methods for registering 2D imagery to 3D point clouds are first reviewed, after which the mutual MI approach is presented. Particular attention is given to adoption of the Normalised Combined Mutual Information (NCMI) approach as a means to produce a similarity measure that exploits the inherently registered LiDAR intensity and point cloud data so as to improve the robustness of registration between optical imagery and LiDAR data. The effectiveness of local versus global similarity measures is also investigated, as are the transformation models involved in the registration process. An experimental program conducted to evaluate MI-based methods for registering aerial imagery to LiDAR data is reported and the results obtained in two areas with differing terrain and land cover, and with aerial imagery of different resolution and LiDAR data with different point density are discussed. These results demonstrate the potential of the MI and especially the CMI methods for registration of imagery and 3D point clouds, and they highlight the feasibility and robustness of the presented MI-based approach to automated registration of multi-sensor, multi-temporal and multi-resolution remote sensing data for a wide range of applications. Numéro de notice : A2014-082 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.11.015 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.11.015 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32987
in ISPRS Journal of photogrammetry and remote sensing > vol 88 (February 2014) . - pp 28 - 40[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2014021 RAB Revue Centre de documentation En réserve L003 Disponible Colorisation de nuages de points 3D par recalage dense d’images numériques / Nathalie Crombez in Traitement du signal, vol 31 n° 1-2 (2014-1-2)
[article]
Titre : Colorisation de nuages de points 3D par recalage dense d’images numériques Type de document : Article/Communication Auteurs : Nathalie Crombez, Auteur ; Guillaume Caron, Auteur ; El-Mustapha Mouaddib, Auteur Année de publication : 2014 Article en page(s) : pp 81 - 106 Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] Amiens
[Termes IGN] cathédrale
[Termes IGN] couleur (variable spectrale)
[Termes IGN] couleur à l'écran
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] image numérique
[Termes IGN] image terrestre
[Termes IGN] patrimoine immobilier
[Termes IGN] photométrie
[Termes IGN] recalage d'image
[Termes IGN] recalage de données localisées
[Termes IGN] semis de pointsRésumé : (auteur) Le patrimoine architectural est composé de biens historiques et artistiques qui doivent être protégés, préservés, restaurés et exposés au plus grand nombre. Des appareils modernes tels que les scanners à laser 3D sont de plus en plus utilisés en documentation culturelle. Ces outils permettent de générer avec précision et rapidité des nuages de points de monuments historiques. Avec les données collectées, il est possible de créer un maillage afin de visualiser virtuellement les formes et/ou la surface de l’édifice. La plupart du temps, le scanner tridimensionnel est équipé d’un appareil photo numérique qui est utilisé pour coloriser les points relevés. Cependant, la qualité photométrique du nuage de points n’est pas toujours suffisante principalement à cause de problèmes de couleurs et de résolution. Des méthodes d’uniformisation d’intensités existent pour améliorer la colorimétrie mais ne permettent pas d’obtenir des rendus photo-réalistes et d’améliorer la résolution. C’est pourquoi, nous proposons une nouvelle méthode pour coloriser les nuages de points à l’aide d’images numériques de haute résolution acquises avec un appareil photo. Pour cela, nous avons développé une méthode permettant d’obtenir un recalage précis entre des images numériques acquises et un nuage de points, ce qui est une étape cruciale pour une bonne colorisation par projection de couleurs. Des résultats sur des jeux de données issus de la numérisation de la cathédrale d’Amiens à l’intérieur et à l’extérieur démontrent la validité de notre approche en obtenant des nuages de points de qualité et de résolution photométriques nettement meilleures. Numéro de notice : A2014-456 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3166/TS.31.81-10 En ligne : http://gretsi.fr/data/ts/pdf/2014_31_1_70248_1.pdf Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80832
in Traitement du signal > vol 31 n° 1-2 (2014-1-2) . - pp 81 - 106[article]Combining RapidEye and lidar satellite imagery for mapping of mining and mine reclamation / Aaron E. Maxwell in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 2 (February 2014)
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Titre : Combining RapidEye and lidar satellite imagery for mapping of mining and mine reclamation Type de document : Article/Communication Auteurs : Aaron E. Maxwell, Auteur ; Thimoty A. Warner, Auteur ; Michael P. Strager, Auteur Année de publication : 2014 Article en page(s) : pp 179 - 189 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse combinatoire (maths)
[Termes IGN] données lidar
[Termes IGN] image RapidEye
[Termes IGN] mine
[Termes IGN] occupation du sol
[Termes IGN] précision de la classification
[Termes IGN] séparateur à vaste marge
[Termes IGN] visualisation cartographiqueRésumé : (Auteur) The combination of RapidEye satellite imagery and light detection and ranging (lidar) derivatives was assessed for mapping land-cover within a mountaintop coal surface mine complex in the southern coalfields of West Virginia, USA. Support vector machines (SVM), random forests (RF), and boosted classification and regression trees (CART) algorithms were used. Incorporation of the lidar-derived data increased map accuracy in comparison to using only the five imagery bands, and SVM generally produced a more accurate classification than the ensemble tree algorithms based on overall map accuracy, Kappa statistics, allocation disagreement, quantity disagreement, and McNemar's test of statistical significance. Based on measures of predictor variable importance within the ensemble tree classifiers, the normalized digital surface model (nDSM) was found to be more useful than first return intensity data for differentiating the classes Numéro de notice : A2014-111 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.80.2.179-189 En ligne : https://doi.org/10.14358/PERS.80.2.179-189 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33016
in Photogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 2 (February 2014) . - pp 179 - 189[article]Filtering airborne lidar data by modified white top-hat transform with directional edge constraints / Yong Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 2 (February 2014)
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Titre : Filtering airborne lidar data by modified white top-hat transform with directional edge constraints Type de document : Article/Communication Auteurs : Yong Li, Auteur ; Bin Yong, Auteur ; Huayi Wu, Auteur Année de publication : 2014 Article en page(s) : pp 133 - 141 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] contrainte géométrique
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] morphologie mathématique
[Termes IGN] objet géographique ponctuel
[Termes IGN] programmation par contraintes
[Termes IGN] sous-solRésumé : (Auteur) A novel algorithm that employs modified white top-hat (MWTH) transform with directional edge constraints is proposed in this study to automatically extract ground points from airborne light detection and ranging (lidar) data. MWTH transform can effectively distinguish above-ground objects that are smaller than the window size and higher than the height difference threshold. Directional edge constraints significantly decrease omission errors from protruding ground features. Incorporating MWTH transform and directional edge constraints enables the simultaneous consideration of the size, height, and edge characteristics of lidar data for judging above-ground objects. Experimental results verify that the proposed algorithm exhibits promising performance and high accuracy in various complicated landscapes, even in areas with dramatic changes in elevation. The proposed algorithm has minimal omission and commission error oscillation for different test sites, thereby demonstrating its stability and reliability in a wide range of applications. Numéro de notice : A2014-108 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.80.2.133-141 En ligne : https://doi.org/10.14358/PERS.80.2.133-141 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33013
in Photogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 2 (February 2014) . - pp 133 - 141[article]Multiple-entity based classification of airborne laser scanning data in urban areas / S. Xu in ISPRS Journal of photogrammetry and remote sensing, vol 88 (February 2014)
[article]
Titre : Multiple-entity based classification of airborne laser scanning data in urban areas Type de document : Article/Communication Auteurs : S. Xu, Auteur ; M. George Vosselman, Auteur ; Sander J. Oude Elberink, Auteur Année de publication : 2014 Article en page(s) : pp 1 - 15 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse multicritère
[Termes IGN] classificateur paramétrique
[Termes IGN] classification automatique d'objets
[Termes IGN] données laser
[Termes IGN] données localisées 3D
[Termes IGN] extraction de la végétation
[Termes IGN] image ALOS-PALSAR
[Termes IGN] milieu urbain
[Termes IGN] test de performanceRésumé : (Auteur) There are two main challenges when it comes to classifying airborne laser scanning (ALS) data. The first challenge is to find suitable attributes to distinguish classes of interest. The second is to define proper entities to calculate the attributes. In most cases, efforts are made to find suitable attributes and less attention is paid to defining an entity. It is our hypothesis that, with the same defined attributes and classifier, accuracy will improve if multiple entities are used for classification. To verify this hypothesis, we propose a multiple-entity based classification method to classify seven classes: ground, water, vegetation, roof, wall, roof element, and undefined object. We also compared the performance of the multiple-entity based method to the single-entity based method. Features have been extracted, in most previous work, from a single entity in ALS data; either from a point or from grouped points. In our method, we extract features from three different entities: points, planar segments, and segments derived by mean shift. Features extracted from these entities are inputted into a four-step classification strategy. After ALS data are filtered into ground and non-ground points. Features generalised from planar segments are used to classify points into the following: water, ground, roof, vegetation, and undefined objects. This is followed by point-wise identification of the walls and roof elements using the contextual information of a building. During the contextual reasoning, the portion of the vegetation extending above the roofs is classified as a roof element. This portion of points is eventually re-segmented by the mean shift method and then reclassified. Five supervised classifiers are applied to classify the features extracted from planar segments and mean shift segments. The experiments demonstrate that a multiple-entity strategy achieves slightly higher overall accuracy and achieves much higher accuracy for vegetation, in comparison to the single-entity strategy (using only point features and planar segment features). Although the multiple-entity method obtains nearly the same overall accuracy as the planar-segment method, the accuracy of vegetation improves by 3.3% with the rule-based classifier. The multiple-entity method obtains much higher overall accuracy and higher accuracy in vegetation in comparison to using only the point-wise classification method for all five classifiers. Meanwhile, we compared the performances of five classifiers. The rule-based method provides the highest overall accuracy at 97.0%. The rule-based method provides over 99.0% accuracy for the ground and roof classes, and a minimum accuracy of 90.0% for the water, vegetation, wall and undefined object classes. Notably, the accuracy of the roof element class is only 70% with the rule-based method, or even lower with other classifiers. Most roof elements have been assigned to the roof class, as shown in the confusion matrix. These erroneous assignments are not fatal errors because both a roof and a roof element are part of a building. In addition, a new feature which indicates the average point space within the planar segment is generalised to distinguish vegetation from other classes. Its performance is compared to the percentage of points with multiple pulse count in planar segments. Using the feature computed with only average point space, the detection rate of vegetation in a rule-based classifier is 85.5%, which is 6% lower than that with pulse count information. Numéro de notice : A2014-080 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.11.008 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.11.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32985
in ISPRS Journal of photogrammetry and remote sensing > vol 88 (February 2014) . - pp 1 - 15[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2014021 RAB Revue Centre de documentation En réserve L003 Disponible Photogrammetry or lidar? / Fabrice Marre in GEO: Geoconnexion international, vol 13 n° 2 (february 2014)PermalinkPutting stock in your survey / Bernard Draeyer in GEO: Geoconnexion international, vol 13 n° 2 (february 2014)PermalinkPermalinkAmélioration de la localisation 3D de données laser terrestre à l'aide de cartes 2D ou modèles 3D / Fabrice Monnier (2014)PermalinkPermalinkAn algorithm for automatic detection of pole-like street furniture objects from Mobile Laser Scanner point clouds / C. Cabo in ISPRS Journal of photogrammetry and remote sensing, vol 87 (January 2014)PermalinkAnalyse sémantique de nuages de points 3D dans le milieu urbain : sol, façades, objets urbains et accessibilité / Andres Felipe Serna Morales (2014)PermalinkContextual classification of lidar data and building object detection in urban areas / Joachim Niemeyer in ISPRS Journal of photogrammetry and remote sensing, vol 87 (January 2014)PermalinkEstimation de la biomasse aérienne à partir de données lidar aéroporté / António Ferraz in Revue Française de Photogrammétrie et de Télédétection, n° 205 (Janvier 2014)PermalinkPermalinkFusion of airborne laserscanning point clouds and images for supervised and unsupervised scene classification / Markus Gerke in ISPRS Journal of photogrammetry and remote sensing, vol 87 (January 2014)PermalinkIndividual tree segmentation over large areas using airborne LiDAR point cloud and very high resolution optical imagery / Yuchu Qin (2014)PermalinkIntegrating disparate lidar data at the national scale to assess the relationships between height above ground, land cover and ecoregions / Jason M. Stocker in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 1 (January 2014)PermalinkIntégration des systèmes d’acquisition de données topographiques par scanner laser dynamique dans les processus de mesure et de contrôle des gabarits de la SNCF / Quentin Choquart (2014)PermalinkLarge scale road network extraction in forested moutainous areas using airborne laser scanning data / António Ferraz (2014)PermalinkLiDAR-derived surface roughness texture mapping: Application to mount St. Helens Pumice Plain deposit analysis / Patrick L. Whelley in IEEE Transactions on geoscience and remote sensing, vol 52 n° 1 tome 2 (January 2014)PermalinkMethod comparisons of forest attribute estimations based on different remote sensing sources, including Lidar. The Vosges case study / Nicolas Py (2014)PermalinkNewly recorded Neolithic earthen long barrows in south-western Poland: unexpected discoveries, expanded perspectives, new interprétations / Agnieszka Przybył (2014)PermalinkNumérisation 3D de bâtiments / Raphaële Heno (2014)PermalinkReconstruction de modèles 3D photoréalistes de façades à partir de données image et laser terrestre / Jérôme Demantké (2014)Permalink