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Learning high-level features by fusing multi-view representation of MLS point clouds for 3D object recognition in road environments / Zhipeng Luo in ISPRS Journal of photogrammetry and remote sensing, vol 150 (April 2019)
[article]
Titre : Learning high-level features by fusing multi-view representation of MLS point clouds for 3D object recognition in road environments Type de document : Article/Communication Auteurs : Zhipeng Luo, Auteur ; Jonathan Li, Auteur ; Zhenlong Xiao, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 44 - 58 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] fusion de données
[Termes IGN] jointure spatiale
[Termes IGN] objet 3D
[Termes IGN] reconnaissance d'objets
[Termes IGN] représentation multiple
[Termes IGN] réseau neuronal convolutif
[Termes IGN] semis de pointsRésumé : (Auteur) Most existing 3D object recognition methods still suffer from low descriptiveness and weak robustness although remarkable progress has made in 3D computer vision. The major challenge lies in effectively mining high-level 3D shape features. This paper presents a high-level feature learning framework for 3D object recognition through fusing multiple 2D representations of point clouds. The framework has two key components: (1) three discriminative low-level 3D shape descriptors for obtaining multi-view 2D representation of 3D point clouds. These descriptors preserve both local and global spatial relationships of points from different perspectives and build a bridge between 3D point clouds and 2D Convolutional Neural Networks (CNN). (2) A two-stage fusion network, which consists of a deep feature learning module and two fusion modules, for extracting and fusing high-level features. The proposed method was tested on three datasets, one of which is Sydney Urban Objects dataset and the other two were acquired by a mobile laser scanning (MLS) system along urban roads. The results obtained from comprehensive experiments demonstrated that our method is superior to the state-of-the-art methods in descriptiveness, robustness and efficiency. Our method achieves high recognition rates of 94.6%, 93.1% and 74.9% on the above three datasets, respectively. Numéro de notice : A2019-137 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.01.024 Date de publication en ligne : 16/02/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.01.024 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92468
in ISPRS Journal of photogrammetry and remote sensing > vol 150 (April 2019) . - pp 44 - 58[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2019041 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019043 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019042 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Multilane roads extracted from the OpenStreetMap urban road network using random forests / Yongyang Xu in Transactions in GIS, vol 23 n° 2 (April 2019)
[article]
Titre : Multilane roads extracted from the OpenStreetMap urban road network using random forests Type de document : Article/Communication Auteurs : Yongyang Xu, Auteur ; Zhong Xie, Auteur ; Liang Wu, Auteur ; Zhanlong Chen, Auteur Année de publication : 2019 Article en page(s) : pp 224 - 240 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] apprentissage automatique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données localisées des bénévoles
[Termes IGN] extraction du réseau routier
[Termes IGN] milieu urbain
[Termes IGN] OpenStreetMap
[Termes IGN] Pékin (Chine)
[Termes IGN] réseau routierRésumé : (Auteur) The volunteered geographic information (VGI) collected in OpenStreetMap (OSM) has been used in many applications. Extracting multilane roads and establishing a high level of expressed detail play important roles in the field of automated cartographic generalization. An accurate and detailed extraction process benefits geographic analysis, urban region division, and road network construction, as well as transportation applications services. The road networks in OSM have a high level of detail and complex structures; however, they also include many duplicate lines, which degrade the efficiency and increase the difficulty of extracting multilane roads. To resolve these problems, this work proposes a machine‐learning‐based approach, in which the road networks are first converted from lines to polygons. Then, various geometric descriptors, including compactness, width, circularity, area, perimeter, complexity, parallelism, shape descriptor, and width‐to‐length ratio, are used to train a random forest (RF) classifier and identify the candidates. Finally, another RF is trained to evaluate the candidates using all the geometric descriptors and topological features; the outputs of this second trained RF are the predicted multilane roads. An experiment using OSM data from Beijing, China validated the proposed method, which achieves a highly effective performance when extracting multilane roads from OSM. Numéro de notice : A2019-250 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12514 Date de publication en ligne : 26/12/2018 En ligne : https://doi.org/10.1111/tgis.12514 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93006
in Transactions in GIS > vol 23 n° 2 (April 2019) . - pp 224 - 240[article]Segmentation for Object-Based Image Analysis (OBIA): A review of algorithms and challenges from remote sensing perspective / Mohammad D. Hossain in ISPRS Journal of photogrammetry and remote sensing, vol 150 (April 2019)
[article]
Titre : Segmentation for Object-Based Image Analysis (OBIA): A review of algorithms and challenges from remote sensing perspective Type de document : Article/Communication Auteurs : Mohammad D. Hossain, Auteur ; Dongmei Chen, Auteur Année de publication : 2019 Article en page(s) : pp 115 - 134 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse d'image orientée objet
[Termes IGN] appariement de données localisées
[Termes IGN] apprentissage automatique
[Termes IGN] classification hybride
[Termes IGN] image à haute résolution
[Termes IGN] objet géographique
[Termes IGN] segmentation d'image
[Termes IGN] segmentation en régions
[Termes IGN] segmentation par décomposition-fusionRésumé : (Auteur) Image segmentation is a critical and important step in (GEographic) Object-Based Image Analysis (GEOBIA or OBIA). The final feature extraction and classification in OBIA is highly dependent on the quality of image segmentation. Segmentation has been used in remote sensing image processing since the advent of the Landsat-1 satellite. However, after the launch of the high-resolution IKONOS satellite in 1999, the paradigm of image analysis moved from pixel-based to object-based. As a result, the purpose of segmentation has been changed from helping pixel labeling to object identification. Although several articles have reviewed segmentation algorithms, it is unclear if some segmentation algorithms are generally more suited for (GE)OBIA than others. This article has conducted an extensive state-of-the-art survey on OBIA techniques, discussed different segmentation techniques and their applicability to OBIA. Conceptual details of those techniques are explained along with the strengths and weaknesses. The available tools and software packages for segmentation are also summarized. The key challenge in image segmentation is to select optimal parameters and algorithms that can general image objects matching with the meaningful geographic objects. Recent research indicates an apparent movement towards the improvement of segmentation algorithms, aiming at more accurate, automated, and computationally efficient techniques. Numéro de notice : A2019-138 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.02.009 Date de publication en ligne : 23/02/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.02.009 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92469
in ISPRS Journal of photogrammetry and remote sensing > vol 150 (April 2019) . - pp 115 - 134[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2019041 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019043 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019042 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Vehicle detection in aerial images / Michael Ying Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 4 (avril 2019)
[article]
Titre : Vehicle detection in aerial images Type de document : Article/Communication Auteurs : Michael Ying Yang, Auteur ; Wentong Liao, Auteur ; Xinbo Li, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 297 - 304 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] compréhension de l'image
[Termes IGN] détection d'objet
[Termes IGN] entropie
[Termes IGN] image aérienne
[Termes IGN] orthoimage
[Termes IGN] précision de la classification
[Termes IGN] qualité d'image
[Termes IGN] réseau neuronal convolutif
[Termes IGN] véhicule automobileRésumé : (Auteur) The detection of vehicles in aerial images is widely applied in many applications. Comparing with object detection in the ground view images, vehicle detection in aerial images remains a challenging problem because of small vehicle size and the complex background. In this paper, we propose a novel double focal loss convolutional neural network (DFL-CNN) framework. In the proposed framework, the skip connection is used in the CNN structure to enhance the feature learning. Also, the focal loss function is used to substitute for conventional cross entropy loss function in both of the region proposal network (RPN) and the final classifier. We further introduce the first large-scale vehicle detection dataset ITCVD with ground truth annotations for all the vehicles in the scene. We demonstrate the performance of our model on the existing benchmark German Aerospace Center (DLR) 3K dataset as well as the ITCVD dataset. The experimental results show that our DFL-CNN outperforms the baselines on vehicle detection. Numéro de notice : A2019-163 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.4.297 Date de publication en ligne : 01/04/2019 En ligne : https://doi.org/10.14358/PERS.85.4.297 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92568
in Photogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 4 (avril 2019) . - pp 297 - 304[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2019041 SL Revue Centre de documentation Revues en salle Disponible Building detection and regularisation using DSM and imagery information / Yousif A. Mousa in Photogrammetric record, vol 34 n° 165 (March 2019)
[article]
Titre : Building detection and regularisation using DSM and imagery information Type de document : Article/Communication Auteurs : Yousif A. Mousa, Auteur ; Petra Helmholz, Auteur ; David Belton, Auteur ; Dimitri Bulatov, Auteur Année de publication : 2019 Article en page(s) : pp 85 - 107 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] détection du bâti
[Termes IGN] extraction automatique
[Termes IGN] masque
[Termes IGN] modèle numérique de surface
[Termes IGN] polygone
[Termes IGN] régularisation
[Termes IGN] simplification de contourRésumé : (Auteur) An automatic method for the regularisation of building outlines is presented, utilising a combination of data‐ and model‐driven approaches to provide a robust solution. The core part of the method includes a novel data‐driven approach to generate approximate building polygons from a list of given boundary points. The algorithm iteratively calculates and stores likelihood values between an arbitrary starting boundary point and each of the following boundary points using a function derived from the geometrical properties of a building. As a preprocessing step, building segments have to be identified using a robust algorithm for the extraction of a digital elevation model. Evaluation results on a challenging dataset achieved an average correctness of 96·3% and 95·7% for building detection and regularisation, respectively. Numéro de notice : A2019-454 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/phor.12275 Date de publication en ligne : 26/03/2019 En ligne : https://doi.org/10.1111/phor.12275 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92867
in Photogrammetric record > vol 34 n° 165 (March 2019) . - pp 85 - 107[article]Central place indexing : hierarchical linear indexing systems for mixed-aperture hexagonal discrete global grid systems / Kevin Sahr in Cartographica, vol 54 n° 1 (Spring 2019)PermalinkDeep mapping gentrification in a large Canadian city using deep learning and Google Street View / Lazar Ilic in Plos one, vol 14 n° 3 (March 2019)PermalinkDuPLO: A DUal view Point deep Learning architecture for time series classificatiOn / Roberto Interdonato in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)PermalinkForest degradation and biomass loss along the Chocó region of Colombia / Victoria Meyer in Carbon Balance and Management, vol 14 (March 2019)PermalinkGeospatial data organization methods with emphasis on aperture-3 hexagonal discrete global grid systems / Ali Mahdavi Amiri in Cartographica, vol 54 n° 1 (Spring 2019)PermalinkInferring user tasks in pedestrian navigation from eye movement data in real-world environments / Hua Liao in International journal of geographical information science IJGIS, Vol 33 n° 3-4 (March - April 2019)PermalinkLearning to segment moving objects / Pavel Tokmakov in International journal of computer vision, vol 127 n° 3 (March 2019)PermalinkSemantic understanding of scenes through the ADE20K dataset / Bolei Zhou in International journal of computer vision, vol 127 n° 3 (March 2019)PermalinkA derivative-free optimization-based approach for detecting architectural symmetries from 3D point clouds / Fan Xue in ISPRS Journal of photogrammetry and remote sensing, vol 148 (February 2019)PermalinkImproving LiDAR classification accuracy by contextual label smoothing in post-processing / Nan Li in ISPRS Journal of photogrammetry and remote sensing, vol 148 (February 2019)PermalinkLearning spectral-spatial-temporal features via a recurrent convolutional neural network for change detection in multispectral imagery / Lichao Mou in IEEE Transactions on geoscience and remote sensing, vol 57 n° 2 (February 2019)PermalinkAdvanced Remote Sensing Technology for Synthetic Aperture Radar Applications, Tsunami Disasters, and Infrastructure / Maged Marghany (2019)PermalinkPermalinkAnalyse d’images par méthode de Deep Learning appliquée au contexte routier en conditions météorologiques dégradées / Khouloud Dahmane (2019)PermalinkPermalinkPermalinkChallenges in grassland mowing event detection with multimodal Sentinel images / Anatol Garioud (2019)PermalinkChallenging deep image descriptors for retrieval in heterogeneous iconographic collections / Dimitri Gominski (2019)PermalinkCorrecting rural building annotations in OpenStreetMap using convolutional neural networks / John E. 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December 2018)PermalinkLand cover mapping at very high resolution with rotation equivariant CNNs : Towards small yet accurate models / Diego Marcos in ISPRS Journal of photogrammetry and remote sensing, vol 145 - part A (November 2018)PermalinkMulti-scale object detection in remote sensing imagery with convolutional neural networks / Zhipeng Deng in ISPRS Journal of photogrammetry and remote sensing, vol 145 - part A (November 2018)PermalinkA new deep convolutional neural network for fast hyperspectral image classification / Mercedes Eugenia Paoletti in ISPRS Journal of photogrammetry and remote sensing, vol 145 - part A (November 2018)PermalinkPan-sharpening via deep metric learning / Yinghui Xing in ISPRS Journal of photogrammetry and remote sensing, vol 145 - part A (November 2018)PermalinkSemantic labeling in very high resolution images via a self-cascaded convolutional neural network / Yoncheng Liu in ISPRS Journal of photogrammetry and remote sensing, vol 145 - part A (November 2018)PermalinkA semi-supervised generative framework with deep learning features for high-resolution remote sensing image scene classification / Wei Han in ISPRS Journal of photogrammetry and remote sensing, vol 145 - part A (November 2018)PermalinkA 3D convolutional neural network method for land cover classification using LiDAR and multi-temporal Landsat imagery / Zewei Xu in ISPRS Journal of photogrammetry and remote sensing, vol 144 (October 2018)PermalinkDeep multi-task learning for a geographically-regularized semantic segmentation of aerial images / Michele Volpi in ISPRS Journal of photogrammetry and remote sensing, vol 144 (October 2018)PermalinkAugmented reality meets computer vision : efficient data generation for urban driving scenes / Hassan Abu Alhaija in International journal of computer vision, vol 126 n° 9 (September 2018)PermalinkConfigurable 3D scene synthesis and 2D image rendering with per-pixel ground truth using stochastic grammars / Chenfanfu Jiang in International journal of computer vision, vol 126 n° 9 (September 2018)PermalinkDescriptive measures of point distributions summarized with respect to spatial scale in visualization / Yukio Sadahiro in Cartographica, vol 53 n° 3 (Fall 2018)PermalinkEstimation of winter wheat crop growth parameters using time series Sentinel-1A SAR data / P. Kumar in Geocarto international, vol 33 n° 9 (September 2018)PermalinkFusion of images and point clouds for the semantic segmentation of large-scale 3D scenes based on deep learning / Rui Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 143 (September 2018)PermalinkImage-based synthesis for deep 3D human pose estimation / Grégory Rogez in International journal of computer vision, vol 126 n° 9 (September 2018)PermalinkSpatial mining of migration patterns from web demographics / T. Edwin Chow in International journal of geographical information science IJGIS, vol 32 n° 9-10 (September - October 2018)Permalink3-D deep learning approach for remote sensing image classification / Amina Ben Hamida in IEEE Transactions on geoscience and remote sensing, vol 56 n° 8 (August 2018)PermalinkAdaptive correlation filters with long-term and short-term memory for object tracking / Chao Ma in International journal of computer vision, vol 126 n° 8 (August 2018)PermalinkA deep learning approach to DTM extraction from imagery using rule-based training labels / Caroline M. Gevaert in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)PermalinkA deep neural network with spatial pooling (DNNSP) for 3-D point cloud classification / Zhen Wang in IEEE Transactions on geoscience and remote sensing, vol 56 n° 8 (August 2018)PermalinkSpectral-spatial classification of hyperspectral images using wavelet transform and hidden Markov random fields / Elham Kordi Ghasrodashti in Geocarto international, vol 33 n° 8 (August 2018)PermalinkCombining machine-learning topic models and spatiotemporal analysis of social media data for disaster footprint and damage assessment / Bernd Resch in Cartography and Geographic Information Science, Vol 45 n° 4 (July 2018)PermalinkEvolutionary approach for detection of buried remains using hyperspectral images / Leon Dozal in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 7 (juillet 2018)PermalinkExploring geo-tagged photos for land cover validation with deep learning / Hanfa Xing in ISPRS Journal of photogrammetry and remote sensing, vol 141 (July 2018)PermalinkExtracting leaf area index using viewing geometry effects : A new perspective on high-resolution unmanned aerial system photography / Lukas Roth in ISPRS Journal of photogrammetry and remote sensing, vol 141 (July 2018)PermalinkHierarchical cellular automata for visual saliency / Yao Qin in International journal of computer vision, vol 126 n° 7 (July 2018)PermalinkA light and faster regional convolutional neural network for object detection in optical remote sensing images / Peng Ding in ISPRS Journal of photogrammetry and remote sensing, vol 141 (July 2018)PermalinkMining and visual exploration of closed contiguous sequential patterns in trajectories / Can Yang in International journal of geographical information science IJGIS, vol 32 n° 7-8 (July - August 2018)PermalinkTesting time-geographic density estimation for home range analysis using an agent-based model of animal movement / Joni A. Downs in International journal of geographical information science IJGIS, vol 32 n° 7-8 (July - August 2018)PermalinkApplication of deep learning for object detection / Ajeet Ram Pathak in Procedia Computer Science, vol 132 (2018)PermalinkClassification à très large échelle d’images satellites à très haute résolution spatiale par réseaux de neurones convolutifs / Tristan Postadjian in Revue Française de Photogrammétrie et de Télédétection, n° 217-218 (juin - septembre 2018)PermalinkFusion tardive d’images SPOT 6/7 et de données multitemporelles Sentinel-2 pour la détection de la tache urbaine / Cyril Wendl in Revue Française de Photogrammétrie et de Télédétection, n° 217-218 (juin - septembre 2018)PermalinkA voxel- and graph-based strategy for segmenting man-made infrastructures using perceptual grouping laws: comparison and evaluation / Yusheng Xu in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 6 (juin 2018)PermalinkAn object-based approach for mapping forest structural types based on low-density LiDAR and multispectral imagery / Luis Angel Ruiz in Geocarto international, vol 33 n° 5 (May 2018)PermalinkClassifying airborne LiDAR point clouds via deep features learned by a multi-scale convolutional neural network / Ruibin Zhao in International journal of geographical information science IJGIS, vol 32 n° 5-6 (May - June 2018)PermalinkDeep convolutional neural network training enrichment using multi-view object-based analysis of Unmanned Aerial systems imagery for wetlands classification / Tao Liu in ISPRS Journal of photogrammetry and remote sensing, vol 139 (May 2018)PermalinkDo semantic parts emerge in convolutional neural networks? / Abel Gonzalez-Garcia in International journal of computer vision, vol 126 n° 5 (May 2018)PermalinkA geometric-based approach for road matching on multi-scale datasets using a genetic algorithm / Alireza Chehreghan in Cartography and Geographic Information Science, Vol 45 n° 3 (May 2018)PermalinkLarge-scale supervised learning for 3D Point cloud labeling : Semantic3d.Net / Timo Hackel in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 5 (mai 2018)Permalink