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Voxel-optimized regional water vapor tomography and comparison with radiosonde and numerical weather model / Biyan Chen in Journal of geodesy, vol 88 n° 7 (July 2014)
[article]
Titre : Voxel-optimized regional water vapor tomography and comparison with radiosonde and numerical weather model Type de document : Article/Communication Auteurs : Biyan Chen, Auteur ; Z. Liu, Auteur Année de publication : 2014 Article en page(s) : pp 691 - 703 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal
[Termes IGN] positionnement par GNSS
[Termes IGN] tomographie
[Termes IGN] vapeur d'eau
[Termes IGN] voxelRésumé : (Auteur)Water vapor tomography has been developed as a powerful tool to model spatial and temporal distribution of atmospheric water vapor. Global navigation satellite systems (GNSS) water vapor tomography refers to the 3D structural construction of tropospheric water vapor using a large number of GNSS signals that penetrate the tomographic modeling area from different positions. The modeling area is usually discretized into a number of voxels. A major issue involved is that some voxels are not crossed by any GNSS signal rays, resulting in an undetermined solution to the tomographic system. To alleviate this problem, the number of voxels crossed by GNSS signal rays should be as large as possible. An important way to achieve this is to optimize the geographic distribution of tomographic voxels. We propose an approach to optimize voxel distribution in both vertical and horizontal domains. In the vertical domain, water vapor profiles derived from radiosonde data are exploited to identify the maximum height of tomography and the optimal vertical resolution. In the horizontal domain, the optimal horizontal distribution of voxels is obtained by searching the maximum number of ray-crossing voxels in both latitude and longitude directions. The water vapor tomography optimization procedures are implemented using GPS water vapor data from the Hong Kong Satellite Positioning Reference Station Network. The tomographic water vapor fields solved from the optimized tomographic voxels are evaluated using radiosonde data and a numerical weather prediction non-hydrostatic model (NHM) obtained for the Hong Kong station. The comparisons of tomographic integrated water vapor (IWV) with the radiosonde and NHM IWV show that RMS errors of their differences are 1.41 and 3.09 mm, respectively. Moreover, the tomographic water vapor density results are compared with those of radiosonde and NHM. The RMS error of the density differences between tomography and radiosonde data is 1.05 g/m3 . For the comparison between tomography and NHM, an overall RMS error of 1.43g/m3 is achieved. Numéro de notice : A2014-415 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-014-0713-0 Date de publication en ligne : 08/04/2014 En ligne : https://doi.org/10.1007/s00190-014-0713-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73956
in Journal of geodesy > vol 88 n° 7 (July 2014) . - pp 691 - 703[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 266-2014071 SL Revue Centre de documentation Revues en salle Disponible An 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)
[article]
Titre : An algorithm for automatic detection of pole-like street furniture objects from Mobile Laser Scanner point clouds Type de document : Article/Communication Auteurs : C. Cabo, Auteur ; C. Ordonez, Auteur ; S. Garcia-Cortés, Auteur ; J. Martinez, Auteur Année de publication : 2014 Article en page(s) : pp 47 - 56 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] détection automatique
[Termes IGN] discrétisation
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] mobilier urbain
[Termes IGN] semis de points
[Termes IGN] voxelRésumé : (Auteur) An algorithm for automatic extraction of pole-like street furniture objects using Mobile Laser Scanner data was developed and tested. The method consists in an initial simplification of the point cloud based on the regular voxelization of the space. The original point cloud is spatially discretized and a version of the point cloud whose amount of data represents 20–30% of the total is created. All the processes are carried out with the reduced version of the data, but the original point cloud is always accessible without any information loss, as each point is linked to its voxel. All the horizontal sections of the voxelized point cloud are analyzed and segmented separately. The two-dimensional fragments compatible with a section of a target pole are selected and grouped. Finally, the three-dimensional voxel representation of the detected pole-like objects is identified and the points from the original point cloud belonging to each pole-like object are extracted. The algorithm can be used with data from any Mobile Laser Scanning system, as it transforms the original point cloud and fits it into a regular grid, thus avoiding irregularities produced due to point density differences within the point cloud. The algorithm was tested in four test sites with different slopes and street shapes and features. All the target pole-like objects were detected, with the only exception of those severely occluded by large objects and some others which were either attached or too close to certain features. Numéro de notice : A2014-011 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.10.008 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.10.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32916
in ISPRS Journal of photogrammetry and remote sensing > vol 87 (January 2014) . - pp 47 - 56[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 081-2014011 RAB Revue Centre de documentation En réserve L003 Disponible Fusion 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)
[article]
Titre : Fusion of airborne laserscanning point clouds and images for supervised and unsupervised scene classification Type de document : Article/Communication Auteurs : Markus Gerke, Auteur ; Jing Xiao, Auteur Année de publication : 2014 Article en page(s) : pp 78 - 92 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] champ aléatoire de Markov
[Termes IGN] classification dirigée
[Termes IGN] classification non dirigée
[Termes IGN] classification par arbre de décision
[Termes IGN] conflation
[Termes IGN] densification
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] semis de points
[Termes IGN] toit
[Termes IGN] voxelRésumé : (Auteur) Automatic urban object detection from airborne remote sensing data is essential to process and efficiently interpret the vast amount of airborne imagery and Laserscanning (ALS) data available today. This paper combines ALS data and airborne imagery to exploit both: the good geometric quality of ALS and the spectral image information to detect the four classes buildings, trees, vegetated ground and sealed ground. A new segmentation approach is introduced which also makes use of geometric and spectral data during classification entity definition. Geometric, textural, low level and mid level image features are assigned to laser points which are quantified into voxels. The segment information is transferred to the voxels and those clusters of voxels form the entity to be classified. Two classification strategies are pursued: a supervised method, using Random Trees and an unsupervised approach, embedded in a Markov Random Field framework and using graph-cuts for energy optimization. A further contribution of this paper concerns the image-based point densification for building roofs which aims to mitigate the accuracy problems related to large ALS point spacing. Results for the ISPRS benchmark test data show that to rely on color information to separate vegetation from non-vegetation areas does mostly lead to good results, but in particular in shadow areas a confusion between classes might occur. The unsupervised classification strategy is especially sensitive in this respect. As far as the point cloud densification is concerned, we observe similar sensitivity with respect to color which makes some planes to be missed out, or false detections still remain. For planes where the densification is successful we see the expected enhancement of the outline. Numéro de notice : A2014-014 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.10.011 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.10.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32919
in ISPRS Journal of photogrammetry and remote sensing > vol 87 (January 2014) . - pp 78 - 92[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 081-2014011 RAB Revue Centre de documentation En réserve L003 Disponible Hyperspectral image noise reduction based on rank-1 tensor decomposition / Xian Guoa in ISPRS Journal of photogrammetry and remote sensing, vol 83 (September 2013)
[article]
Titre : Hyperspectral image noise reduction based on rank-1 tensor decomposition Type de document : Article/Communication Auteurs : Xian Guoa, Auteur ; Xian Guo, Auteur ; Xin Huang, Auteur ; Liangpei Zhanga, Auteur ; Lefei Zhang, Auteur Année de publication : 2013 Article en page(s) : pp 50 - 63 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] calcul tensoriel
[Termes IGN] décomposition spatiale
[Termes IGN] filtrage du bruit
[Termes IGN] image hyperspectrale
[Termes IGN] tenseur
[Termes IGN] valeur propre
[Termes IGN] voxelRésumé : (Auteur) In this study, a novel noise reduction algorithm for hyperspectral imagery (HSI) is proposed based on high-order rank-1 tensor decomposition. The hyperspectral data cube is considered as a three-order tensor that is able to jointly treat both the spatial and spectral modes. Subsequently, the rank-1 tensor decomposition (R1TD) algorithm is applied to the tensor data, which takes into account both the spatial and spectral information of the hyperspectral data cube. A noise-reduced hyperspectral image is then obtained by combining the rank-1 tensors using an eigenvalue intensity sorting and reconstruction technique. Compared with the existing noise reduction methods such as the conventional channel-by-channel approaches and the recently developed multidimensional filter, the spatial–spectral adaptive total variation filter, experiments with both synthetic noisy data and real HSI data reveal that the proposed R1TD algorithm significantly improves the HSI data quality in terms of both visual inspection and image quality indices. The subsequent image classification results further validate the effectiveness of the proposed HSI noise reduction algorithm. Numéro de notice : A2013-488 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.06.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.06.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32626
in ISPRS Journal of photogrammetry and remote sensing > vol 83 (September 2013) . - pp 50 - 63[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 081-2013091 RAB Revue Centre de documentation En réserve L003 Disponible 3-D voxel-based solid modeling of a broad-leaved tree for accurate volume estimation using portable scanning lidar / Fumiki Hosoi in ISPRS Journal of photogrammetry and remote sensing, vol 82 (August 2013)
[article]
Titre : 3-D voxel-based solid modeling of a broad-leaved tree for accurate volume estimation using portable scanning lidar Type de document : Article/Communication Auteurs : Fumiki Hosoi, Auteur ; Yohei Nakai, Auteur ; Kenji Omasa, Auteur Année de publication : 2013 Article en page(s) : pp 41 - 48 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] arbre (flore)
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] feuille (végétation)
[Termes IGN] modélisation 3D
[Termes IGN] solide
[Termes IGN] tronc
[Termes IGN] volume (grandeur)
[Termes IGN] voxelRésumé : (Auteur) We developed a method to produce a 3-D voxel-based solid model of a tree based on portable scanning lidar data for accurate estimation of the volume of the woody material. First, we obtained lidar measurements with a high laser pulse density from several measurement positions around the target, a Japanese zelkova tree. Next, we converted lidar-derived point-cloud data for the target into voxels. The voxel size was 0.5 cm x 0.5 cm x 0.5 cm. Then, we used differences in the spatial distribution of voxels to separate the stem and large branches (diameter > 1 cm) from small branches (diameter Numéro de notice : A2013-410 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.04.011 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.04.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32548
in ISPRS Journal of photogrammetry and remote sensing > vol 82 (August 2013) . - pp 41 - 48[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 081-2013081 RAB Revue Centre de documentation En réserve L003 Disponible Combining terrestrial stereophotogrammetry, DGPS and GIS-based 3D voxel modelling in the volumetric recording of archaeological features / H. Orengo in ISPRS Journal of photogrammetry and remote sensing, vol 76 (February 2013)PermalinkA new 3-D solar radiation model for 3-D city models / Jaroslav Hofierka in Transactions in GIS, vol 16 n° 5 (October 2012)PermalinkTree topology representation from TLS point clouds using depth-first search in voxel space / A. Schilling in Photogrammetric Engineering & Remote Sensing, PERS, vol 78 n° 4 (April 2012)PermalinkPhotogrammetric techniques for voxel-based flow velocity field measurement / Patrick Westfeld in Photogrammetric record, vol 26 n° 136 (December 2011 - February 2012)Permalink4D GPS water vapor tomography: new parameterized approaches / Donat Perler in Journal of geodesy, vol 85 n° 8 (August 2011)PermalinkIncremental segmentation of lidar point clouds with an octree-structured voxel space / M. Wang in Photogrammetric record, vol 26 n° 133 (March - May 2011)PermalinkA volumetric approach to change in satellite images / T. Pollard in Photogrammetric Engineering & Remote Sensing, PERS, vol 76 n° 7 (July 2010)PermalinkPermalinkPermalink3D simulation of soft-objects / D.Y. Shen in International journal of geographical information science IJGIS, vol 20 n° 3 (march 2006)Permalink