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Utilisation conjointe de trains d'ondes LiDAR vert et infrarouge pour la bathymétrie des eaux de très faibles profondeurs / Tristan Allouis in Revue Française de Photogrammétrie et de Télédétection, n° 213 - 214 (janvier - avril 2017)
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Titre : Utilisation conjointe de trains d'ondes LiDAR vert et infrarouge pour la bathymétrie des eaux de très faibles profondeurs Type de document : Article/Communication Auteurs : Tristan Allouis, Auteur ; Jean-Stéphane Bailly, Auteur ; Yves Pastol, Auteur ; Catherine Le Roux, Auteur Année de publication : 2017 Article en page(s) : pp 33 - 42 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] bathymétrie laser
[Termes IGN] données lidar
[Termes IGN] lidar bathymétrique
[Termes IGN] littoral
[Termes IGN] problème d'unité zonale modifiable
[Termes IGN] rivière
[Termes IGN] traitement du signalRésumé : (auteur) La bathymétrie et la topographie des surfaces immergées sont des connaissances essentielles pour la gestion durable des rivières et des espaces littoraux. Parmi les techniques permettant de les obtenir, le LiDAR bathymétrique apparaît prometteur par sa capacité à relever de grandes surfaces en un temps limité, avec une forte résolution spatiale et de manière continue entre zones émergées et immergées. Bien que certaines études aient porté sur la précision de cette technique dans les zones côtières de profondeur modérée, peu se sont intéressées aux eaux très peu profondes ( Numéro de notice : A2017-046 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.52638/rfpt.2017.362 Date de publication en ligne : 27/04/2017 En ligne : https://doi.org/10.52638/rfpt.2017.362 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84224
in Revue Française de Photogrammétrie et de Télédétection > n° 213 - 214 (janvier - avril 2017) . - pp 33 - 42[article]Weakly supervised segmentation-aided classification of urban scenes from 3D LIDAR point clouds / Stéphane Guinard (2017)
contenu dans ISPRS Hannover Workshop: HRIGI 17 – CMRT 17 – ISA 17 – EuroCOW 17 / Christian Heipke (2017)
Titre : Weakly supervised segmentation-aided classification of urban scenes from 3D LIDAR point clouds Type de document : Article/Communication Auteurs : Stéphane Guinard , Auteur ; Loïc Landrieu , Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2017 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 42-1/W1 Conférence : ISPRS 2017 Workshops HRIGI – CMRT – ISA – EuroCOW 06/06/2017 09/06/2017 Hanovre Allemagne ISPRS OA Archives Importance : pp 151 - 157 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] carte de confiance
[Termes IGN] champ aléatoire conditionnel
[Termes IGN] classification dirigée
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] scène urbaine
[Termes IGN] segmentation
[Termes IGN] semis de pointsRésumé : (auteur) We consider the problem of the semantic classification of 3D LiDAR point clouds obtained from urban scenes when the training set is limited. We propose a non-parametric segmentation model for urban scenes composed of anthropic objects of simple shapes, partionning the scene into geometrically-homogeneous segments which size is determined by the local complexity. This segmentation can be integrated into a conditional random field classifier (CRF) in order to capture the high-level structure of the scene. For each cluster, this allows us to aggregate the noisy predictions of a weakly-supervised classifier to produce a higher confidence data term. We demonstrate the improvement provided by our method over two publicly-available large-scale data sets. Numéro de notice : C2017-034 Affiliation des auteurs : LASTIG MATIS (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLII-1-W1-151-2017 Date de publication en ligne : 31/05/2017 En ligne : https://doi.org/10.5194/isprs-archives-XLII-1-W1-151-2017 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89327 Mapping individual tree health using full-waveform airborne laser scans and imaging spectroscopy: A case study for a floodplain eucalypt forest / Iurii Shendryk in Remote sensing of environment, vol 187 (15 December 2016)
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Titre : Mapping individual tree health using full-waveform airborne laser scans and imaging spectroscopy: A case study for a floodplain eucalypt forest Type de document : Article/Communication Auteurs : Iurii Shendryk, Auteur ; Mark Broich, Auteur ; Mirela G. Tulbure, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 202 - 217 Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] Australie
[Termes IGN] classification orientée objet
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] dépérissement
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] Eucalyptus (genre)
[Termes IGN] Eucalyptus camaldulensis
[Termes IGN] inondation
[Termes IGN] spectrométrieRésumé : (auteur) Declining forest health can affect crucial ecosystem functions, such as carbon storage in biomass and soils, the regulation of water regimes, the modulation of regional climate and conservation of biodiversity. Airborne laser scanning (ALS) and imaging spectroscopy (IS) are two potentially complementary remote sensing technologies capable of characterizing and monitoring regional forest health. However, the combined use of ALS and IS data to classify the health of individual trees has not yet been assessed. In this study we propose a new approach utilizing ALS and IS combined to characterize the health of individual trees. Firstly, we applied a recently developed bottom-up individual tree delineation algorithm across a structurally complex floodplain eucalypt forest that has experienced episodes of severe dieback over the past six decades. We further calculated ALS and IS indices for delineated tree crowns and used them as predictor variables in machine learning models. We trained and evaluated an object-oriented random forest classifier against field-measured tree crown dieback and transparency ratios, as indicators of eucalypt tree health and crown density, respectively. Our results showed that dieback levels of individual trees can be classified using ALS and IS with an overall accuracy of 81% and a kappa score of 0.66, while the classification of tree crown transparency levels had an overall accuracy of 70% and a kappa score of 0.5. Returned pulse width, intensity and density related ALS indices were the most important predictors in the tree health classification, as they accounted for > 40% of the variance in the data. At the forest level in terms of dieback, 81.5% of correctly delineated trees were classified as healthy, 12.3% as declining and 6.2% as dying or dead. Dieback occurred primarily in areas that were flooded Numéro de notice : A2016-767 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2016.10.014 En ligne : http://dx.doi.org/10.1016/j.rse.2016.10.014 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82411
in Remote sensing of environment > vol 187 (15 December 2016) . - pp 202 - 217[article]Adaptive estimation of the stable boundary layer height using combined Lidar and microwave radiometer observations / Umar Saeed in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)
[article]
Titre : Adaptive estimation of the stable boundary layer height using combined Lidar and microwave radiometer observations Type de document : Article/Communication Auteurs : Umar Saeed, Auteur ; Francesc Rocadenbosch, Auteur ; Susanne Crewell, Auteur Année de publication : 2016 Article en page(s) : pp 6895 - 6906 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] analyse de données
[Termes IGN] analyse diachronique
[Termes IGN] données lidar
[Termes IGN] radiomètre à hyperfréquence
[Termes IGN] Satellite Microwave RadiometerRésumé : (Auteur) A synergetic approach for the estimation of stable boundary layer height (SBLH) using lidar and microwave radiometer (MWR) data is presented. Vertical variance of the backscatter signal from a ceilometer is used as an indicator of the aerosol stratification in the nocturnal stable boundary layer. This hypothesis is supported by a statistical analysis over one month of observations. Thermodynamic information from the MWR-derived potential temperature is incorporated as coarse estimate of the SBLH. Data from the two instruments are adaptively assimilated by using an extended Kalman filter (EKF). A first test of the algorithm is performed by applying it to collocated Vaisala CT25K ceilometer and humidity and temperature profiler MWR data collected during the HD(CP)2 Observational Prototype Experiment (HOPE) campaign at Jülich, Germany. The application of the algorithm to different atmospheric scenarios reveals the superior performance of the EKF compared to a nonlinear least squares estimator, particularly in nonidealized conditions. Numéro de notice : A2016-920 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2586298 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2586298 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83324
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 12 (December 2016) . - pp 6895 - 6906[article]Automatic parameter selection for intensity-based registration of imagery to LiDAR data / Ebadat Ghanbari Parmehr in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)
[article]
Titre : Automatic parameter selection for intensity-based registration of imagery to LiDAR data Type de document : Article/Communication Auteurs : Ebadat Ghanbari Parmehr, Auteur ; Clive Simpson Fraser, Auteur ; Chunsun Zhang, Auteur Année de publication : 2016 Article en page(s) : pp 7032 - 7043 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] appariement d'images
[Termes IGN] appariement de données localisées
[Termes IGN] densité de probabilité
[Termes IGN] données lidar
[Termes IGN] image aérienne
[Termes IGN] image binaire
[Termes IGN] segmentation binaire
[Termes IGN] semis de pointsRésumé : (Auteur) Automatic registration of multisensor data, for example, imagery and Light Detection And Ranging (LiDAR), is a basic step in data fusion in the field of geospatial information processing. Mutual information (MI) has recently attracted research attention as a statistical similarity measure for intensity-based registration of multisensor images in the related fields of computer vision and remote sensing. Since MI-based registration methods rely on joint probability density functions (pdfs) for the data sets, errors in pdf estimation can affect the MI value, causing registration failure due to the presence of nonmonotonic surfaces of similarity measure. The quality of the estimated pdf is highly dependent upon both the bin size and the smoothing technique used in the pdf estimation procedure. The lack of a general approach to assign an appropriate bin size value for the pdf of multisensor data reduces both the level of automation and the robustness of the registration. In this paper, a novel bin size selection approach is proposed to improve registration reliability. The proposed method determines the best (uniform or variable) bin size for the pdf estimation via an analysis of the relationship between the similarity measure values of the data and the adopted geometric transformation. This highlights the role of the component of MI sensitive to the transformation, rather than the MI component that is unrelated to the transformation, such as noise. The performance of the proposed method for the registration of aerial imagery to LiDAR point clouds is investigated, and experimental results are compared with those achieved through a feature-based registration method. Numéro de notice : A2016-923 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2594294 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2594294 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83327
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 12 (December 2016) . - pp 7032 - 7043[article]Statistical inference for forest structural diversity indices using airborne laser scanning data and the k-Nearest Neighbors technique / Matteo Mura in Remote sensing of environment, vol 186 (1 December 2016)PermalinkSystematic effects in laser scanning and visualization by confidence regions / Karl Rudolf Koch in Journal of applied geodesy, vol 10 n° 4 (December 2016)PermalinkThe effects of temporal differences between map and ground data on map-assisted estimates of forest area and biomass / Ronald E. McRoberts in Annals of Forest Science, vol 73 n° 4 (December 2016)PermalinkThe open data HELI-DEM DTM for the western alpine area: computation and publication / L. Biagi in Applied geomatics, vol 8 n° 3-4 (December 2016)PermalinkEffective number of layers: A new measure for quantifying three-dimensional stand structure based on sampling with terrestrial LiDAR / Martin Ehbrecht in Forest ecology and management, vol 380 (15 november 2016)PermalinkDu nuage de points à la représentation 3D avec PostGIS / Tom Van Tilburg in Géomatique expert, n° 113 (novembre - décembre 2016)PermalinkRapid updating and improvement of airborne LIDAR DEMs through ground-based SfM 3-D modeling of volcanic features / Stephan Kolzenburg in IEEE Transactions on geoscience and remote sensing, vol 54 n° 11 (November 2016)PermalinkTraitement des nuages de points sous PostGIS / Ludovic Delauné in Géomatique expert, n° 113 (novembre - décembre 2016)PermalinkAccuracy of tree geometric parameters depending on the LiDAR data density / Edyta Hadas in European journal of remote sensing, vol 49 n° 1 (2016)PermalinkAméliorer la perception du réalisme dans la géovisualisation du littoral : Utilisation de données spatiotemporelles hétérogènes / Antoine Masse in Revue internationale de géomatique, vol 26 n° 4 (octobre - décembre 2016)PermalinkAn intensity recovery algorithm (IRA) for minimizing the edge effect of LIDAR data / Fabiane Bordin in European journal of remote sensing, vol 49 n° 1 (2016)PermalinkAn operational high-resolution forest inventory / Julianno Sambatti in GIM international, vol 30 n° 10 (October 2016)PermalinkAutomatic registration of MLS point clouds and SfM meshes of urban area / Reiji Yoshimura in Geo-spatial Information Science, vol 19 n° 3 (October 2016)PermalinkEffect of flying altitude, scanning angle and scanning mode on the accuracy of ALS based forest inventory / Juha Keränen in International journal of applied Earth observation and geoinformation, vol 52 (October 2016)PermalinkEffects of forest structure and airborne laser scanning point cloud density on 3D delineation of individual tree crowns / Kaja Kandare in European journal of remote sensing, vol 49 n° 1 (2016)PermalinkA functional regression model for inventories supported by aerial laser scanner data or photogrammetric point clouds / Magnussen, Steen in Remote sensing of environment, vol 184 (October 2016)PermalinkInterurban visibility diagnosis from point clouds / Oscar Iglesias in European journal of remote sensing, vol 49 n° 1 (2016)PermalinkRelative importance analysis of Landsat, waveform LIDAR and PALSAR inputs for deciduous biomass estimation / Alyssa Endres in European journal of remote sensing, vol 49 n° 1 (2016)PermalinkA robust approach for tree segmentation in deciduous forests using small-footprint airborne LiDAR data / Hamid Hamraz in International journal of applied Earth observation and geoinformation, vol 52 (October 2016)PermalinkTechnology in focus: bathymetric lidar / Anonyme in GIM international, vol 30 n° 10 (October 2016)PermalinkA vision for smart cities / Ruedi Wagner in GEO: Geoconnexion international, vol 15 n° 9 (October 2016)PermalinkLidar detection of individual tree size in tropical forests / António Ferraz in Remote sensing of environment, vol 183 (15 September 2016)PermalinkAn individual tree-based automated registration of aerial images to LiDAR Data in a forested area / Jun-Hak Lee in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 9 (September 2016)PermalinkDynamic occlusion detection and inpainting of in situ captured terrestrial laser scanning point clouds sequence / Chi Chen in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)PermalinkEstimating the solar transmittance of urban trees using airborne LiDAR and radiative transfer simulation / Haruki Oshio in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)PermalinkInternational benchmarking of the individual tree detection methods for modeling 3-D canopy structure for silviculture and forest ecology using airborne laser scanning / Yunsheng Wang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)PermalinkLocal-scale flood mapping on vegetated floodplains from radiometrically calibrated airborne LiDAR data / Radosław Malinowski in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)PermalinkReconstruction en 3D des bâtiments à partir des données Lidar / M. A. Missomi in Géomatique expert, n° 112 (septembre - octobre 2016)PermalinkSlicing method for curved façade and window extraction from point clouds / S.M. Iman Zolanvari in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)PermalinkAirborne lidar estimation of aboveground forest biomass in the absence of field inventory / António Ferraz in Remote sensing, vol 8 n° 8 (August 2016)PermalinkA local structure and direction-aware optimization approach for three-dimensional tree modeling / Zhen Wang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 8 (August 2016)PermalinkUnsupervised classification of airborne laser scanning data to locate potential wildlife habitats for forest management planning / Jari Vauhkonen in Forestry, an international journal of forest research, vol 89 n° 4 (August 2016)PermalinkClassifying buildings from point clouds and images / Evangelos Maltezos in GIM international, vol 30 n° 7 (July 2016)PermalinkFusion of LiDAR orthowaveforms and hyperspectral imagery for shallow river bathymetry and turbidity estimation / Zhigang Pan in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)PermalinkA hierarchical approach to three-dimensional segmentation of LiDAR data at single-tree level in a multilayered forest / Claudia Paris in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)PermalinkImproved progressive TIN densification filtering algorithm for airborne LiDAR data in forested areas / Xiaoqian Zhao in ISPRS Journal of photogrammetry and remote sensing, vol 117 (July 2016)PermalinkLidar imagery and InSAR for digital forestry / Benoît Saint-Onge in GIM international, vol 30 n° 7 (July 2016)PermalinkNationwide airborne laser scanning based models for volume, biomass and dominant height in Finland / Eetu Kotivuori in Silva fennica, vol 50 n° 4 (2016)PermalinkA new adaptive method to filter terrestrial laser scanner point clouds using morphological filters and spectral information to conserve surface micro-topography / Emilio Rodríguez-Caballero in ISPRS Journal of photogrammetry and remote sensing, vol 117 (July 2016)PermalinkA novel computer-aided tree species identification method based on burst wind segmentation of 3D bark textures / Alice Ahlem Othmani in Machine Vision and Applications, vol 27 n° 5 (July 2016)PermalinkOpenBIM framework for a collaborative historic preservation system / Shawn E. O'Keeffe in International journal of 3-D information modeling, vol 5 n° 4 (October - December 2016)PermalinkWildlife management using aiborne Lidar / Joan Hagar in GIM international, vol 30 n° 7 (July 2016)PermalinkSimultaneous detection and tracking of pedestrian from panoramic laser scanning data / Wen Xiao in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol III-3 (July 2016)PermalinkLe 12ème Forum de la topographie : topographie et BIM / Tania Landes in XYZ, n° 147 (juin - août 2016)PermalinkAn intelligent geospatial processing unit for image classification based on geographic vector agents (GVAs) / Kambiz Borna in Transactions in GIS, vol 20 n° 3 (June 2016)Permalink