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Progressive visualization of complex 3D models over the internet / Jing Chen in Transactions in GIS, vol 20 n° 6 (December 2016)
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Titre : Progressive visualization of complex 3D models over the internet Type de document : Article/Communication Auteurs : Jing Chen, Auteur ; Jiawei Li, Auteur ; Mo Li, Auteur Année de publication : 2016 Article en page(s) : pp 887 - 902 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] données maillées
[Termes IGN] données multiéchelles
[Termes IGN] image numérique
[Termes IGN] niveau de détail
[Termes IGN] rendu automatisé
[Termes IGN] simulation 3D
[Termes IGN] Web 3D Service
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) The online transmission and real-time rendering of complex 3D models have always been a bottleneck which limits the performance of Web 3D simulation systems. To improve the efficiency of data transmission and mesh reconstruction, this article proposes a novel progressive mesh structure. In the first stage of progressive visualization, the base data and the base index generated by vertex clustering simplification are transmitted to the client for the fundamental rendering. Then the incremental data and corresponding indexes at higher levels are transmitted, as the viewpoint approaches the simulation object. The multi-scale incremental data organization benefits the performance and efficiency of the Web 3D simulation system by separately transmitting and reconstructing the corresponding level of mesh details. To demonstrate the adaptability and reliability of this algorithm, we developed an experimental prototype system to conduct a series of experiments. The results of experiments show that the improved progressive mesh structure described in this article takes good advantage of the vertex clustering simplification scheme to increase the efficiency of online transmission and mesh reconstruction, and the average frame rate of the progressive visualization has been increased to some extent, especially for massive data in large scale scenes. Numéro de notice : A2016-996 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12185 En ligne : https://doi.org/10.1111/tgis.12185 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83776
in Transactions in GIS > vol 20 n° 6 (December 2016) . - pp 887 - 902[article]A robust background regression based score estimation algorithm for hyperspectral anomaly detection / Zhao Rui in ISPRS Journal of photogrammetry and remote sensing, vol 122 (December 2016)
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Titre : A robust background regression based score estimation algorithm for hyperspectral anomaly detection Type de document : Article/Communication Auteurs : Zhao Rui, Auteur ; Bo Du, Auteur ; Liangpei Zhang, Auteur ; Lefei Zhang, Auteur Année de publication : 2016 Article en page(s) : pp 126 – 144 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] détection d'anomalie
[Termes IGN] image hyperspectrale
[Termes IGN] régressionRésumé : (Auteur) Anomaly detection has become a hot topic in the hyperspectral image analysis and processing fields in recent years. The most important issue for hyperspectral anomaly detection is the background estimation and suppression. Unreasonable or non-robust background estimation usually leads to unsatisfactory anomaly detection results. Furthermore, the inherent nonlinearity of hyperspectral images may cover up the intrinsic data structure in the anomaly detection. In order to implement robust background estimation, as well as to explore the intrinsic data structure of the hyperspectral image, we propose a robust background regression based score estimation algorithm (RBRSE) for hyperspectral anomaly detection. The Robust Background Regression (RBR) is actually a label assignment procedure which segments the hyperspectral data into a robust background dataset and a potential anomaly dataset with an intersection boundary. In the RBR, a kernel expansion technique, which explores the nonlinear structure of the hyperspectral data in a reproducing kernel Hilbert space, is utilized to formulate the data as a density feature representation. A minimum squared loss relationship is constructed between the data density feature and the corresponding assigned labels of the hyperspectral data, to formulate the foundation of the regression. Furthermore, a manifold regularization term which explores the manifold smoothness of the hyperspectral data, and a maximization term of the robust background average density, which suppresses the bias caused by the potential anomalies, are jointly appended in the RBR procedure. After this, a paired-dataset based k-nn score estimation method is undertaken on the robust background and potential anomaly datasets, to implement the detection output. The experimental results show that RBRSE achieves superior ROC curves, AUC values, and background-anomaly separation than some of the other state-of-the-art anomaly detection methods, and is easy to implement in practice. Numéro de notice : A2016--023 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.10.006 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2016.10.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83886
in ISPRS Journal of photogrammetry and remote sensing > vol 122 (December 2016) . - pp 126 – 144[article]The 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)
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Titre : The effects of temporal differences between map and ground data on map-assisted estimates of forest area and biomass Type de document : Article/Communication Auteurs : Ronald E. McRoberts, Auteur ; Erik Naesset, Auteur ; Terje Gobakken, Auteur Année de publication : 2016 Article en page(s) : pp 839 - 847 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse diachronique
[Termes IGN] biomasse
[Termes IGN] données de terrain
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] estimation statistique
[Termes IGN] forêt
[Termes IGN] image Landsat
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] Minnesota (Etats-Unis)
[Termes IGN] Norvège
[Termes IGN] régression
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) Key message : When areas of interest experience little change, remote sensing-based maps whose dates deviate from ground data can still substantially enhance precision. However, when change is substantial, deviations in dates reduce the utility of such maps for this purpose.
Context : Remote sensing-based maps are well-established as means of increasing the precision of estimates of forest inventory parameters. The general practice is to use maps whose dates correspond closely to the dates of ground data. However, as national forest inventories move to continuous inventories, deviations between map and ground data dates increase.
Aims : The aim was to assess the degree to which remote sensing-based maps can be used to increase the precision of estimates despite differences between map and ground data dates.
Methods : For study areas in the USA and Norway, maps were constructed for each of two dates, and model-assisted regression estimators were used to estimate inventory parameters using ground data whose dates differed by as much as 11 years from the map dates.
Results : For the Minnesota study area that had little change, 7-year differences in dates had little effect on the precision of estimates of proportion forest area. For the Norwegian study area that experienced considerable change, 11-year differences in dates had a detrimental effect on the precision of estimates of mean biomass per unit area.
Conclusions : The effects of differences in map and ground data dates were less important than temporal change in the study area.Numéro de notice : A2016--168 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s13595-015-0485-6 Date de publication en ligne : 12/05/2015 En ligne : https://doi.org/10.1007/s13595-015-0485-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87210
in Annals of Forest Science > vol 73 n° 4 (December 2016) . - pp 839 - 847[article]The practical application of 3D vision in the field: Measuring reindeer (rangifer tarandus) antler growth velocities / Derek D. Lichti in Photogrammetric record, vol 31 n° 156 (December 2016 - February 2017)
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Titre : The practical application of 3D vision in the field: Measuring reindeer (rangifer tarandus) antler growth velocities Type de document : Article/Communication Auteurs : Derek D. Lichti, Auteur ; Jeremy Steward, Auteur ; Jacky C. K. Chow, Auteur ; John Matyas, Auteur Année de publication : 2016 Article en page(s) : pp 394 - 406 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] analyse diachronique
[Termes IGN] caméra 3D temps-de-vol
[Termes IGN] image 3D
[Termes IGN] image optique
[Termes IGN] semis de pointsRésumé : (auteur) Advances in three-dimensional (3D) optical imaging have made possible precise and accurate measurements of many scenes, ranging from engineering to architecture to art. However, measurements of some 3D objects are more difficult to obtain than others, particularly if the edges do not feature regular geometry, the colour is dark and variable, and if the object moves haphazardly. Such objects occur regularly in biology, and the present study illustrates some of the challenges of evaluating such objects. The growing antlers of three live reindeer (Rangifer tarandus) is presented as an example of how 3D imaging, specifically time-of-flight range imaging, can be used to solve to a reasonable extent a problem that is very difficult to approximate using traditional techniques. Mean antler growth velocities of the order of 7 to 9 mm/day were estimated, using the proposed methodology, from data of these three animals collected over a seven-week period. Numéro de notice : A2016--003 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12167 Date de publication en ligne : 07/11/2016 En ligne : https://doi.org/10.1111/phor.12167 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83821
in Photogrammetric record > vol 31 n° 156 (December 2016 - February 2017) . - pp 394 - 406[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 106-2016041 RAB Revue Centre de documentation En réserve L003 Disponible Three-dimensional deformation monitoring of urban infrastructure by tomographic SAR using multitrack TerraSAR-X data stacks / Sina Montazeri in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)
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Titre : Three-dimensional deformation monitoring of urban infrastructure by tomographic SAR using multitrack TerraSAR-X data stacks Type de document : Article/Communication Auteurs : Sina Montazeri, Auteur ; Xiao Xiang Zhu, Auteur ; Michael Eineder, Auteur ; Richard Bamler, Auteur Année de publication : 2016 Article en page(s) : pp 6868 - 6878 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] analyse comparative
[Termes IGN] Berlin
[Termes IGN] déformation d'édifice
[Termes IGN] image radar moirée
[Termes IGN] image TerraSAR-X
[Termes IGN] semis de points
[Termes IGN] surveillance d'ouvrage
[Termes IGN] tomographie radarRésumé : (Auteur) Differential synthetic aperture radar tomography (D-TomoSAR), similar to its conventional counterparts such as differential interferometric SAR and persistent scatterer interferometry, is only capable of capturing 1-D deformation along the satellite's line of sight. In this paper, we propose a method based on L1-norm minimization within local spatial cubes to reconstruct 3-D displacement vectors from TomoSAR point clouds available from at least three different viewing geometries. The methodology is applied on two pairs of cross-heading-combination of ascending and descending-TerraSAR-X (TS-X) spotlight image stacks over the city of Berlin. The linear deformation rate and the amplitude of seasonal deformation are decomposed, and the results from two test sites with remarkable deformation pattern are discussed in detail. The results, to our knowledge, demonstrate the first attempt for motion decomposition using TomoSAR data from multiple viewing geometries. Numéro de notice : A2016-919 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2585741 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2585741 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83322
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 12 (December 2016) . - pp 6868 - 6878[article]Urban slum detection using texture and spatial metrics derived from satellite imagery / Divyani Kohli in Journal of spatial science, vol 61 n° 2 (December 2016)PermalinkAssimilation of SMOS retrievals in the land information system / Clay B. Blankenship in IEEE Transactions on geoscience and remote sensing, vol 54 n° 11 (November 2016)PermalinkBlind hyperspectral unmixing using total variation and ℓq sparse regularization / Jakob Sigurdsson in IEEE Transactions on geoscience and remote sensing, vol 54 n° 11 (November 2016)PermalinkClose-range photogrammetric tools for epigraphic surveys / Mariam Samaan in Journal on Computing and Cultural Heritage, JOCCH, vol 9 n° 3 (November 2016)PermalinkGeometric calibration of Ziyuan-3 three-line cameras using ground control lines / Jinshan Cao in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 11 (November 2016)PermalinkA global study of NDVI difference among moderate-resolution satellite sensors / Xingwang Fan in ISPRS Journal of photogrammetry and remote sensing, vol 121 (November 2016)PermalinkA method for automated snow avalanche debris detection through use of synthetic aperture radar (SAR) imaging / Hannah Vickers in Earth and space science, vol 3 n° 11 (November 2016)PermalinkMultiple kernel learning based on discriminative kernel clustering for hyperspectral band selection / Jie Feng in IEEE Transactions on geoscience and remote sensing, vol 54 n° 11 (November 2016)PermalinkRelevé topographique des environnements urbains [article originellement paru dans le numéro mai/juin 2016 de la revue italienne GEOMedia] / Luigi Colombo in Géomatique expert, n° 113 (novembre - décembre 2016)PermalinkRobust multitask learning with three-dimensional empirical mode decomposition-based features for hyperspectral classification / Zhi He in ISPRS Journal of photogrammetry and remote sensing, vol 121 (November 2016)PermalinkSemi-supervised hyperspectral classification from a small number of training samples using a co-training approach / Michał Romaszewski in ISPRS Journal of photogrammetry and remote sensing, vol 121 (November 2016)PermalinkWave period and coastal bathymetry using wave propagation on optical images / Céline Danilo in IEEE Transactions on geoscience and remote sensing, vol 54 n° 11 (November 2016)PermalinkAn operational high-resolution forest inventory / Julianno Sambatti in GIM international, vol 30 n° 10 (October 2016)PermalinkAutomatic segment-level tree species recognition using high resolution aerial winter imagery / Anton Kuzmin in European journal of remote sensing, vol 49 n° 1 (2016)PermalinkA Computationally efficient algorithm for fusing multispectral and hyperspectral images / Raúl Guerra in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)PermalinkDeep feature extraction and classification of hyperspectral images based on convolutional neural networks / Yushi Chen in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)PermalinkDevelopment of a large-format UAS imaging system with the construction of a one sensor geometry from a multicamera array / Jiann-Yeou Rau in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)PermalinkDisaster debris estimation using high-resolution polarimetric stereo-SAR / Christian N. Koyama in ISPRS Journal of photogrammetry and remote sensing, vol 120 (october 2016)PermalinkDistributed texture-based land cover classification algorithm using hidden Markov model for multispectral data / S. Jenicka in Survey review, vol 48 n° 351 (October 2016)PermalinkEvaluating EO1-Hyperion capability for mapping conifer and broadleaved forests / Nicola Puletti in European journal of remote sensing, vol 49 n° 1 (2016)PermalinkFast and accurate target detection based on multiscale saliency and active contour model for high-resolution SAR images / Song Tu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)PermalinkHabitat change on Horn Island, Mississippi, 1940-2010, determined from textural features in panchromatic vertical aerial imagery / Guy W. Jeter Jr in Geocarto international, Vol 31 n° 9 - 10 (October - November 2016)PermalinkImage processing and GIS techniques applied to high resolution satellite data for lineament mapping of thermal power plant site in Allahabad district, U.P., India / Aniruddha Uniyal in Geocarto international, Vol 31 n° 9 - 10 (October - November 2016)PermalinkInfluence of tree species complexity on discrimination performance of vegetation indices / Azadeh Ghiyamat in European journal of remote sensing, vol 49 n° 1 (2016)PermalinkObject-based morphological profiles for classification of remote sensing imagery / Christian Geiss in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 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)PermalinkSAR image change detection based on correlation kernel and multistage extreme learning machine / Lu Jia in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)PermalinkSemisupervised classification for hyperspectral image based on multi-decision labeling and deep feature learning / Xiaorui Ma in ISPRS Journal of photogrammetry and remote sensing, vol 120 (october 2016)PermalinkA tensor decomposition-based anomaly detection algorithm for hyperspectral image / Xing Zhang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)PermalinkAccuracy assessment of NOAA coastal change analysis program 2006 - 2010 land cover and land cover change data / John W. McCombs in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 9 (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)PermalinkAutomatic rough georeferencing of multiview oblique and vertical aerial image datasets of urban scenes / Styliani Verykokou in Photogrammetric record, vol 31 n° 155 (September - November 2016)PermalinkBlending zone determination for aerial orthimage mosaicking / Chao-Hung Lin in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)PermalinkUne carrière dans les drones / Anonyme in Géomatique expert, n° 112 (septembre - octobre 2016)PermalinkCorrection of ZY-3 image distortion caused by satellite jitter via virtual steady reimaging using attitude data / Mi Wang in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)PermalinkDistance measure based change detectors for polarimetric SAR imagery / Yonghong Zhang in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 9 (September 2016)PermalinkEstimating forest species abundance through linear unmixing of CHRIS/PROBA imagery / S. Stagakis in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)PermalinkEvaluation par imagerie satellitaire de la dynamique spatiale du parc marin des mangroves de la république Démocratique du Congo entre 2006 et 2015 / B.M. Kalambay in Afrique Science, vol 12 n° 5 (septembre - octobre 2016)PermalinkFloristic composition and across-track reflectance gradient in Landsat images over Amazonian forests / Javier Muro in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)PermalinkGeometric calibration of a hyperspectral frame camera / Raquel A. de Oliveira in Photogrammetric record, vol 31 n° 155 (September - November 2016)PermalinkMapping of land cover in northern California with simulated hyperspectral satellite imagery / Matthew L. Clark in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)PermalinkA methodology for near real-time change detection between Unmanned Aerial Vehicle and wide area satellite images / Anastasios L. Fytsilis in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)PermalinkNoise removal from hyperspectral image with joint spectral–spatial distributed sparse representation / Jie Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)PermalinkRegression wavelet analysis for lossless coding of remote-sensing data / Naoufal Amrani in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)PermalinkRemote sensing data as a potential source for establishment of the 3D cadastre in Slovenia / Petra Dobrež in Geodetski vestnik, vol 60 n° 3 (September - November 2016)Permalink