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Robust locally weighted regression techniques for ground surface points filtering in mobile laser scanning three dimensional point cloud data / Abdul Nurunnabi in IEEE Transactions on geoscience and remote sensing, vol 54 n° 4 (April 2016)
[article]
Titre : Robust locally weighted regression techniques for ground surface points filtering in mobile laser scanning three dimensional point cloud data Type de document : Article/Communication Auteurs : Abdul Nurunnabi, Auteur ; Geoff West, Auteur ; David Belton, Auteur Année de publication : 2016 Article en page(s) : pp 2181 - 2193 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] ajustement de paramètres
[Termes IGN] algorithme de filtrage
[Termes IGN] analyse comparative
[Termes IGN] analyse de données
[Termes IGN] extraction de points
[Termes IGN] régression
[Termes IGN] segmentation d'image
[Termes IGN] semis de points
[Termes IGN] télémétrie laser mobile
[Termes IGN] zone urbaineRésumé : (Auteur) This paper introduces robust algorithms for extracting the ground points in laser scanning 3-D point cloud data. Global polynomial functions have been used for filtering algorithms for point cloud data; however, it is not suitable as it may lead to bias for the filtering algorithms and can cause misclassification errors when many different objects are present. In this paper, robust statistical approaches are coupled with locally weighted 2-D regression that fits without any predefined global function for the variables of interest. Algorithms are performed iteratively on 2-D profiles: x - z and y - z. The z (elevation) values are robustly down weighted based on the residuals for the fitted points. The new set of down-weighted z values, along with the corresponding x (or y) values, is used to get a new fit for the lower surface level. The process of fitting and down weighting continues until the difference between two consecutive fits is insignificant. The final fit is the required ground level, and the ground surface points are those that fall within the ground level and the level after adding some threshold value with the ground level for z values. Experimental results are compared with the recently proposed segmentation method through simulated and real mobile laser scanning point clouds from urban areas that include many objects that appear in road scenes such as short walls, large buildings, electric poles, signposts, and cars. Results show that the proposed robust methods efficiently extract ground surface points with better than 97% accuracy. Numéro de notice : A2016-840 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2496972 En ligne : http://dx.doi.org/10.1109/TGRS.2015.2496972 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82884
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 4 (April 2016) . - pp 2181 - 2193[article]A feature selection approach for segmentation of very high-resolution satellite images / Ahmad Izadipour in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 3 (March 2016)
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Titre : A feature selection approach for segmentation of very high-resolution satellite images Type de document : Article/Communication Auteurs : Ahmad Izadipour, Auteur ; Behzad Akbari, Auteur ; Barat Mojaradi, Auteur Année de publication : 2016 Article en page(s) : pp 213 - 222 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image Geoeye
[Termes IGN] image Quickbird
[Termes IGN] résolution globale (imagerie)
[Termes IGN] segmentation d'imageRésumé : (auteur) Most of the feature selection (FS) methods in the literature determine features that are appropriate only for a given dataset. In contrast, in this paper a FS method that is not dependent to a specific dataset is proposed. In this regard, the effective feature types based on reasonable facts are predefined and appropriate candidate features for each feature type are selected. In proposed method, the features selected from a single labeled image can be used in segmentation of images captured by different satellites with similar spatial resolution. The selected feature types contain spatial and spectral features. The selected features are applied for segmentation of the images captured by QuickBird and GeoEye satellites and obtained results of proposed method are compared with well-known FS methods. Using different evaluation measures, our comparison shows the efficiency of the proposed method in providing better segmentation compared to other FS methods that are presented in this paper. Numéro de notice : A2016-178 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.82.3.213 En ligne : https://doi.org/10.14358/PERS.82.3.213 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80519
in Photogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 3 (March 2016) . - pp 213 - 222[article]A land use/land cover change geospatial cyberinfrastructure to integrate big data and temporal topology / Jin Xing in International journal of geographical information science IJGIS, vol 30 n° 3-4 (March - April 2016)
[article]
Titre : A land use/land cover change geospatial cyberinfrastructure to integrate big data and temporal topology Type de document : Article/Communication Auteurs : Jin Xing, Auteur ; Renee E. Sieber, Auteur Année de publication : 2016 Article en page(s) : pp 573 - 593 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] changement d'occupation du sol
[Termes IGN] cyberinfrastructure
[Termes IGN] dimension temporelle
[Termes IGN] données maillées
[Termes IGN] données massives
[Termes IGN] données spatiotemporelles
[Termes IGN] optimisation (mathématiques)
[Termes IGN] relation topologique
[Termes IGN] segmentation d'imageRésumé : (Auteur) Big data have shifted spatial optimization from a purely computational-intensive problem to a data-intensive challenge. This is especially the case for spatiotemporal (ST) land use/land cover change (LUCC) research. In addition to greater variety, for example, from sensing platforms, big data offer datasets at higher spatial and temporal resolutions; these new offerings require new methods to optimize data handling and analysis. We propose a LUCC-based geospatial cyberinfrastructure (GCI) that optimizes big data handling and analysis, in this case with raster data. The GCI provides three levels of optimization. First, we employ spatial optimization with graph-based image segmentation. Second, we propose ST Atom Model to temporally optimize the image segments for LUCC. At last, the first two domain ST optimizations are supported by the computational optimization for big data analysis. The evaluation is conducted using DMTI (DMTI Spatial Inc.) Satellite StreetView imagery datasets acquired for the Greater Montreal area, Canada in 2006, 2009, and 2012 (534 GB, 60 cm spatial resolution, RGB image). Our LUCC-based GCI builds an optimization bridge among LUCC, ST modelling, and big data. Numéro de notice : A2016-204 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2015.1104534 En ligne : https://doi.org/10.1080/13658816.2015.1104534 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79891
in International journal of geographical information science IJGIS > vol 30 n° 3-4 (March - April 2016) . - pp 573 - 593[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2016021 RAB Revue Centre de documentation En réserve L003 Disponible Octree-based segmentation for terrestrial LiDAR point cloud data in industrial applications / Yun-Ting Su in ISPRS Journal of photogrammetry and remote sensing, vol 113 (March 2016)
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Titre : Octree-based segmentation for terrestrial LiDAR point cloud data in industrial applications Type de document : Article/Communication Auteurs : Yun-Ting Su, Auteur ; James Bethel, Auteur ; Shuowen Hu, Auteur Année de publication : 2016 Article en page(s) : pp 59 - 74 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] métrologie industrielle
[Termes IGN] octree
[Termes IGN] segmentation d'image
[Termes IGN] semis de points
[Termes IGN] télémétrie laser terrestreRésumé : (auteur) Automated and efficient algorithms to perform segmentation of terrestrial LiDAR data is critical for exploitation of 3D point clouds, where the ultimate goal is CAD modeling of the segmented data. In this work, a novel segmentation technique is proposed, starting with octree decomposition to recursively divide the scene into octants or voxels, followed by a novel split and merge framework that uses graph theory and a series of connectivity analyses to intelligently merge components into larger connected components. The connectivity analysis, based on a combination of proximity, orientation, and curvature connectivity criteria, is designed for the segmentation of pipes, vessels, and walls from terrestrial LiDAR data of piping systems at industrial sites, such as oil refineries, chemical plants, and steel mills. The proposed segmentation method is exercised on two terrestrial LiDAR datasets of a steel mill and a chemical plant, demonstrating its ability to correctly reassemble and segregate features of interest. Numéro de notice : A2016-530 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.01.001 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2016.01.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81612
in ISPRS Journal of photogrammetry and remote sensing > vol 113 (March 2016) . - pp 59 - 74[article]Uniformity-based superpixel segmentation of hyperspectral images / Arun M. Saranathan in IEEE Transactions on geoscience and remote sensing, vol 54 n° 3 (March 2016)
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Titre : Uniformity-based superpixel segmentation of hyperspectral images Type de document : Article/Communication Auteurs : Arun M. Saranathan, Auteur ; Mario Parente, Auteur Année de publication : 2016 Article en page(s) : pp 1419 - 1430 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de groupement
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] analyse infrapixellaire
[Termes IGN] données géologiques
[Termes IGN] image AVIRIS
[Termes IGN] image hyperspectrale
[Termes IGN] segmentation d'imageRésumé : (Auteur) Superpixel segmentation algorithms attempt to group contiguous image pixels which are in homogeneous regions into segments (superpixels). Superpixel segmentation maps have proven successful in improving the performance of unmixing algorithms on hyperspectral images. For hyperspectral images (HSIs), segment members must contain spectrally similar pixels, a requirement we refer to as segment uniformity. Existing superpixel segmentation algorithms which have been applied to HSIs provide no guarantees on the uniformity inside segments. In the absence of such guarantees, the only viable option is to make the segments small enough that uniformity is always ensured; this leads to an oversegmentation of the image. An accurate uniformity measure would lead to a more accurate segmentation. We propose a graph-based agglomerative approach that enforces segment uniformity by setting a threshold for maximum variability inside segments. The threshold is computed by a statistical analysis of the within-class and between-class spectral divergences of several mineral families of interest. We show that the proposed algorithm can be used to generate parsimonious segmentations and facilitate the computation of accurate mineralogical summaries for several simulated and real HSIs of terrestrial and planetary geological surfaces. Numéro de notice : A2016-123 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2480863 En ligne : https://doi.org/10.1109/TGRS.2015.2480863 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80003
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 3 (March 2016) . - pp 1419 - 1430[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2016031 SL Revue Centre de documentation Revues en salle Disponible Identification and utilization of land-use type importance for land-use data generalization / Wenxiu Gao in Cartographic journal (the), Vol 53 n° 1 (February 2016)PermalinkA region-line primitive association framework for object-based remote sensing image analysis / Wang Min in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 2 (February 2016)PermalinkSeamline determination for high resolution orthoimage mosaicking using watershed segmentation / Wang Mi in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 2 (February 2016)PermalinkSGM-based seamline determination for urban orthophoto mosaicking / Shiyan Pang in ISPRS Journal of photogrammetry and remote sensing, vol 112 (February 2016)PermalinkA computational introduction to digital image processing / Alasdair McAndrew (2016)PermalinkContributions à la segmentation non supervisée d'images hyperspectrales : trois approches algébriques et géométriques / Saadallah El Asmar (2016)PermalinkPermalinkMicrowave unmixing with video segmentation for inferring broadleaf and needleleaf brightness temperatures and abundances from mixed forest observations / Lingjia Gu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 1 (January 2016)PermalinkPointwise approach for texture analysis and characterization from very high resolution remote sensing images / Minh-Tan Pham (2016)PermalinkPermalink