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A parallel algorithm for coverage optimization on multi-core architectures / Ran Wei in International journal of geographical information science IJGIS, vol 30 n° 3-4 (March - April 2016)
[article]
Titre : A parallel algorithm for coverage optimization on multi-core architectures Type de document : Article/Communication Auteurs : Ran Wei, Auteur ; Alan T. Murray, Auteur Année de publication : 2016 Article en page(s) : pp 432 - 450 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] architecture de système
[Termes IGN] Californie (Etats-Unis)
[Termes IGN] couverture (données géographiques)
[Termes IGN] cyberinfrastructure
[Termes IGN] Ohio (Etats-Unis)
[Termes IGN] optimisation spatiale
[Termes IGN] système d'information géographique
[Termes IGN] traitement parallèleRésumé : (Auteur) Location siting is an important part of service provision, with much potential to impact operational efficiency, safety, security, system reliability, etc. A class of location models seeks to optimize coverage of demand for service that is continuously distributed across space. Decision-making and planning contexts include police/fire resource allocation for a community, siting cellular towers to support cell phone signal transmission, locating emergency warning sirens to alert the public of severe weather and other related dangers, and many others as well. When facilities can be sited anywhere in continuous space to provide coverage to an entire region, this is a very computationally challenging problem to solve because potential demand for service is everywhere and there are an infinite number of potential facility sites to consider. This article develops a new parallel solution approach for this location coverage optimization problem through an iterative bounding scheme on multi-core architectures. The developed approach is applied to site emergency warning sirens in Dublin, Ohio, and fire stations in Elk Grove, California. Results demonstrate the effectiveness and efficiency of the proposed approach, enabling real-time analysis and planning. This work illustrates that the integration of cyberinfrastructure can significantly improve computational efficiency in solving challenging spatial optimization problems, fitting the themes of this special issue: cyberinfrastructure, GIS, and spatial optimization. Numéro de notice : A2016-202 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2015.1030750 En ligne : https://doi.org/10.1080/13658816.2015.1030750 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79888
in International journal of geographical information science IJGIS > vol 30 n° 3-4 (March - April 2016) . - pp 432 - 450[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2016021 RAB Revue Centre de documentation En réserve L003 Disponible Spatial data fusion in spatial data infrastructures using linked data / Stefan Wiemann in International journal of geographical information science IJGIS, vol 30 n° 3-4 (March - April 2016)
[article]
Titre : Spatial data fusion in spatial data infrastructures using linked data Type de document : Article/Communication Auteurs : Stefan Wiemann, Auteur ; Lars Bernard, Auteur Année de publication : 2016 Article en page(s) : pp 613 - 636 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] données localisées
[Termes IGN] fusion de données
[Termes IGN] géomatique web
[Termes IGN] implémentation (informatique)
[Termes IGN] Open geospatial consortium
[Termes IGN] standard OGC
[Termes IGN] système d'information géographique
[Termes IGN] web des donnéesRésumé : (Auteur) The synthesis of spatial data from the various sources available on the Web is a major challenge for current applications based on Web-based information retrieval and spatial decision-making. This article addresses spatial data fusion, with particular emphasis on its application in Spatial Data Infrastructures (SDIs). Possibilities for the integration of SDI and Semantic Web developments in the context of spatial data fusion are reviewed with a focus on the harmonized description and usage of feature relations. Specifically, potential applications of Linked Data principles are discussed in detail. On this basis, a classification and a decomposition of fusion processes in a service-oriented environment are proposed. A prototype implementation demonstrates the feasibility and usability of the approach using Open Geospatial Consortium (OGC) and Semantic Web standards. Numéro de notice : A2016-095 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2015.1084420 En ligne : https://doi.org/10.1080/13658816.2015.1084420 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79892
in International journal of geographical information science IJGIS > vol 30 n° 3-4 (March - April 2016) . - pp 613 - 636[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2016021 RAB Revue Centre de documentation En réserve L003 Disponible Matrix-based discriminant subspace ensemble for hyperspectral image spatial–spectral feature fusion / Renlong Hang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 2 (February 2016)
[article]
Titre : Matrix-based discriminant subspace ensemble for hyperspectral image spatial–spectral feature fusion Type de document : Article/Communication Auteurs : Renlong Hang, Auteur ; Qingshan Liu, Auteur ; Huihui Song, Auteur ; Yubao Sun, Auteur Année de publication : 2016 Article en page(s) : pp 783 - 794 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse discriminante
[Termes IGN] classification multibande
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] fusion de données
[Termes IGN] image hyperspectrale
[Termes IGN] matriceRésumé : (Auteur) Spatial-spectral feature fusion is well acknowledged as an effective method for hyperspectral (HS) image classification. Many previous studies have been devoted to this subject. However, these methods often regard the spatial-spectral high-dimensional data as 1-D vector and then extract informative features for classification. In this paper, we propose a new HS image classification method. Specifically, matrix-based spatial-spectral feature representation is designed for each pixel to capture the local spatial contextual and the spectral information of all the bands, which can well preserve the spatial-spectral correlation. Then, matrix-based discriminant analysis is adopted to learn the discriminative feature subspace for classification. To further improve the performance of discriminative subspace, a random sampling technique is used to produce a subspace ensemble for final HS image classification. Experiments are conducted on three HS remote sensing data sets acquired by different sensors, and experimental results demonstrate the efficiency of the proposed method. Numéro de notice : A2016-116 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2465899 En ligne : http://dx.doi.org/10.1109/TGRS.2015.2465899 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79996
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 2 (February 2016) . - pp 783 - 794[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2016021 SL Revue Centre de documentation Revues en salle Disponible Autonomous navigation in complex nonplanar environments based on laser ranging / Philipp Andreas Krüsi (2016)
Titre : Autonomous navigation in complex nonplanar environments based on laser ranging Type de document : Thèse/HDR Auteurs : Philipp Andreas Krüsi, Auteur Editeur : Zurich : Eidgenossische Technische Hochschule ETH - Ecole Polytechnique Fédérale de Zurich EPFZ Année de publication : 2016 Note générale : bibliographie
A thesis submitted to attain the degree of doctor of sciences of ETH ZurichLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] algorithme ICP
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] navigation autonome
[Termes IGN] robot mobile
[Termes IGN] semis de points
[Termes IGN] télémètre laser
[Termes IGN] télémétrie laser aéroporté
[Termes IGN] télémétrie laser terrestre
[Termes IGN] temps réel
[Termes IGN] vision par ordinateurRésumé : (auteur) This thesis addresses the problem of autonomous navigation with ground robots in complex environments, which may be characterized as nonplanar and nonstatic. The goal of the presented research is to enable reliable navigation over large distances in generic indoor and outdoor environments, independent of external localization sources such as a global positioning system (GPS). Focusing on these challenges, algorithms for all building blocks of autonomous navigation—localization, mapping, terrain assessment, motion planning, and motion control—are developed, implemented, integrated, and finally evaluated in extensive field experiments. Sensor-based perception of the environment is a basic requirement for localization and mapping. We propose to use a high-frequency three-dimensional (3D) laser scanner as the main exteroceptive sensor. The advantages of this technology lie in the high density and accuracy of the provided measurements, and their independence of lighting and weather conditions. We develop a highly scalable system for six-dimensional (6D) localization and 3D mapping based on iterative closest point (ICP) matching. A topological/metric map representation, where metric information is kept in spatially constrained local submaps representing vertices in a graph, allows to build consistent large-scale maps without requiring global optimization. Long-term application in dynamic and changing environments is enabled by integrating methods for identifying dynamic objects in the scene and for continuously updating existing submaps. Planning feasible and safe motions for a robotic vehicle requires distinguishing obstacles from traversable terrain. We develop two different algorithms for terrain assessment. The first method is targeted at real-time obstacle detection in the vicinity of the robot. Assuming locally planar terrain, a grid-based obstacle map is built by analyzing the raw laser scans. The second approach is based on dense point cloud maps (which can be obtained from the ICP mapping system) and suitable for planar and nonplanar environments. The algorithm computes the geometry and the traversability of the terrain “on demand” at specific query locations, avoiding any artificial discretization or explicit surface reconstruction. The desired terrain characteristics are estimated based on statistics on the local distribution of map points. Given a specific navigation task, motion planning can be defined as the problem of reasoning about how to act based on the knowledge about the environment. This thesis addresses both local obstacle avoidance and global planning over large distances. Our approach to local planning consists of computing a set of candidate trajectories, which are shaped around nearby obstacles or along a given reference path, and enforced to satisfy the robot’s kinematic constraints. The optimal local trajectory is chosen by evaluating the motion alternatives in terms of guidance towards the goal and traversability of the underlying terrain. For global motion planning, we develop an algorithm embedding the proposed point-cloud-based terrain assessment method, which allows trajectories to be directly planned on 3D point cloud maps. The approach is designed to be suitable for generic nonplanar environments, including rough outdoor terrain, multi-level facilities, and more complex geometries. Piecewise continuous trajectories are computed in the full 6D space of robot poses, while strictly considering the vehicle’s kinematic and dynamic constraints. We apply sampling-based planning algorithms to generate an initial trajectory connecting the desired start and goal poses. Subsequently, the trajectory is locally optimized according to a generic cost function, which may include path length, path curvature, and roughness of the traversed terrain. While enforcing the hard constraints to remain satisfied (terrain contact, traversability, kinodynamic feasibility), the trajectory is iteratively deformed until a local minimum of the cost function is reached. We develop two complete systems for autonomous navigation, integrating these approaches. Combining the ICP-based localization and mapping framework with local obstacle detection and local motion planning, we implement a framework for autonomous route following, commonly referred to as teach and repeat (T&R). After a manually controlled teach run, where a graph of local submaps is built, the robot is able to automatically repeat the learned route, using the recorded maps for localization. Unlike classical T&R systems, our framework is suitable for application in dynamic environments, where the integrated obstacle avoidance scheme allows to detect and circumnavigate obstacles appearing on the reference path. In addition to the T&R approach, we present a second navigation system, integrating the point-cloud-based terrain assessment and global planning algorithms with ICP-based localization and mapping. Given a graph of point cloud maps—typically recorded in a manually controlled survey run—the framework enables navigation within the mapped area without being restricted to known routes. Motion control is implemented by a trajectory tracking controller with integrated real-time collision checking. Together with continuous map updates and frequent replanning of the global trajectory, these techniques enable autonomous navigation in nonplanar, nonstatic environments. Finally, we describe the characteristics of the mobile robot ARTOR, which was set up for the purpose of testing and evaluating the developed algorithms under realistic conditions. ARTOR consists of a six-wheeled, electrically powered base vehicle equipped with sensors, computers, and communication gear. The proposed autonomous navigation algorithms were integrated on the robot and tested in extensive field experiments, demonstrating reliable, GPS-independent navigation over large distances and under greatly varying environmental conditions, in unstructured off-road terrain, multi-level environments, and dynamic urban areas. Numéro de notice : 17367 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse étrangère Note de thèse : PhD thesis : Sciences : ETH Zurich : 2016 En ligne : http://dx.doi.org/10.3929/ethz-a-010656081 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84243
Titre : A feature fusion framework for hashing Type de document : Article/Communication Auteurs : I-Hong Jhuo, Auteur ; Li Weng , Auteur ; Wen-Huang Cheng, Auteur ; D.T. Lee, Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2016 Conférence : ICPR 2016, 23rd International Conference on Pattern Recognition 04/12/2016 08/12/2016 Cancun Mexique Proceedings IEEE Importance : pp 2289 - 2294 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] fusion de données
[Termes IGN] graphe
[Termes IGN] mesure de similitudeRésumé : (auteur) A hash algorithm converts data into compact strings. In the multimedia domain, effective hashing is the key to large-scale similarity search in high-dimensional feature space. A limit of existing hashing techniques is that they typically use single features. In order to improve search performance, it is necessary to utilize multiple features. Due to the compactness requirement, concatenation of hash values from different features is not an optimal solution. Thus a fusion process is desired. In this paper, we solve the multiple feature fusion problem by a hash bit selection framework. Given multiple features, we derive an n-bit hash value of improved performance compared with hash values of the same length computed from each individual feature. The framework utilizes a feature-independent hash algorithm to generate a sufficient number of bits from each feature, and selects n bits from the hash bit pool by leveraging pair-wise label information. The metric bit reliability is used for ranking the bits. It is estimated by bit-level hypothesis testing. In addition, we also take into account the dependence among bits. A weighted graph is constructed for refined bit selection, where the bit reliability is used as vertex weights and the mutual information among hash bits is used as edge weights. We demonstrate our framework with LSH. Extensive experiments confirm that our method is effective, and outperforms several state-of-the-art methods. Numéro de notice : C2016-042 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/ICPR.2016.7899977 Date de publication en ligne : 24/04/2017 En ligne : https://doi.org/10.1109/ICPR.2016.7899977 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91854 Fusion of hyperspectral images and digital surface models for urban object extraction / Janja Avbelj (2016)PermalinkFusion of space-borne multi-baseline and multi-frequency interferometric results based on extended Kalman filter to generate high quality DEMs / Xiaojie Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 111 (January 2016)PermalinkPermalinkInstaurer des données, instaurer des publics : une enquête sociologique dans les coulisses de l'open data / Samuel Goeta (2016)PermalinkA merging solution for close-range DEMs to optimize surface coverage and measurement resolution / Stéphane Bertin in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 1 (January 2016)PermalinkParallélisation des processus de traitement des données spatiales / Justin Berli (2016)PermalinkUrban classification by the fusion of thermal infrared hyperspectral and visible data / Jiayi Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 12 (December 2015)PermalinkMapping nocturnal light pollution / Jordi Corbera in GIM international [en ligne], vol 29 n° 11 (November 2015)PermalinkExtraction des zones cohérentes par l’analyse spatio-temporelle d’images de télédétection / Thomas Guyet in Revue internationale de géomatique, vol 25 n° 4 (octobre - décembre 2015)PermalinkFusion of waveform LiDAR data and hyperspectral imagery for land cover classification / Hongzhou Wang in ISPRS Journal of photogrammetry and remote sensing, vol 108 (October 2015)Permalink