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Termes IGN > sciences naturelles > physique > traitement d'image > analyse d'image numérique > extraction de traits caractéristiques > extraction du réseau routier
extraction du réseau routierSynonyme(s)détection du réseau routier |
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Contributions to graph-based hierarchical analysis for images and 3D point clouds / Leonardo Gigli (2021)
Titre : Contributions to graph-based hierarchical analysis for images and 3D point clouds Type de document : Thèse/HDR Auteurs : Leonardo Gigli, Auteur ; Beatriz Marcotegui, Directeur de thèse Editeur : Paris : Université Paris Sciences et Lettres Année de publication : 2021 Importance : 177 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de Doctorat de l'Université PSL, Spécialité : Morphologie MathématiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] analyse de groupement
[Termes IGN] apprentissage automatique
[Termes IGN] arbre aléatoire minimum
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] extraction du réseau routier
[Termes IGN] morphologie mathématique
[Termes IGN] processus de hiérarchisation analytique
[Termes IGN] réseau neuronal de graphes
[Termes IGN] segmentation d'image
[Termes IGN] semis de points
[Termes IGN] texture d'image
[Termes IGN] théorie des graphesIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Graphs are powerful mathematical structures representing a set of objects and the underlying links between pairs of objects somehow related. They are becoming increasingly popular in data science in general and in particular in image or 3D point cloud analysis. Among the wide spectra of applications, they are involved in most of the hierarchical approaches.Hierarchies are particularly important because they allow us to efficiently organize the information required and to analyze the problems at different levels of detail. In this thesis, we address the following topics. Many morphological hierarchical approaches rely on the Minimum Spanning Tree (MST). We propose an algorithm for MST computation in streaming based on a graph decomposition strategy. Thanks to this decomposition, larger images can be processed or can benefit from partial reliable information while the whole image is not completely available.Recent LiDAR developments are able to acquire large-scale and precise 3D point clouds. Many applications, such as infrastructure monitoring, urban planning, autonomous driving, precision forestry, environmental assessment, archaeological discoveries, to cite a few, are under development nowadays. We introduce a ground detection algorithm and compare it with the state of the art. The impact of reducing the point cloud density with low-cost scanners is studied, in the context of an autonomous driving application. Finally, in many hierarchical methods similarities between points are given as input. However, the metric used to compute similarities influences the quality of the final results. We exploit metric learning as a complementary tool that helps to improve the quality of hierarchies. We demonstrate the capabilities of these methods in two contexts. The first one,a texture classification of 3D surfaces. Our approach ranked second in a task organized by SHREC’20 international challenge. The second one learning the similarity function together with the optimal hierarchical clustering, in a continuous feature-based hierarchical clustering formulation. Note de contenu : Introduction
1- Graph theory and clustering
2- Point clouds
3- Ground and road detection
4- Minimum spanning tree for data streams
5- Metric learning
6- Towards Morphological Convolutions on Graphs
ConclusionsNuméro de notice : 28623 Affiliation des auteurs : non IGN Thématique : IMAGERIE/MATHEMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Morphologie Mathématique : Paris Sciences et Lettres : 2021 Organisme de stage : Centre de Morphologie Mathématique DOI : sans En ligne : https://pastel.hal.science/tel-03512298/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99543 FuNet: A novel road extraction network with fusion of location data and remote sensing imagery / Kai Zhou in ISPRS International journal of geo-information, vol 10 n° 1 (January 2021)
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Titre : FuNet: A novel road extraction network with fusion of location data and remote sensing imagery Type de document : Article/Communication Auteurs : Kai Zhou, Auteur ; Yan Xie, Auteur ; Zhan Gao, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 10 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] amélioration du contraste
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] connexité (topologie)
[Termes IGN] extraction du réseau routier
[Termes IGN] fusion d'images
[Termes IGN] itération
[Termes IGN] Pékin (Chine)
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Road semantic segmentation is unique and difficult. Road extraction from remote sensing imagery often produce fragmented road segments leading to road network disconnection due to the occlusion of trees, buildings, shadows, cloud, etc. In this paper, we propose a novel fusion network (FuNet) with fusion of remote sensing imagery and location data, which plays an important role of location data in road connectivity reasoning. A universal iteration reinforcement (IteR) module is embedded into FuNet to enhance the ability of network learning. We designed the IteR formula to repeatedly integrate original information and prediction information and designed the reinforcement loss function to control the accuracy of road prediction output. Another contribution of this paper is the use of histogram equalization data pre-processing to enhance image contrast and improve the accuracy by nearly 1%. We take the excellent D-LinkNet as the backbone network, designing experiments based on the open dataset. The experiment result shows that our method improves over the compared advanced road extraction methods, which not only increases the accuracy of road extraction, but also improves the road topological connectivity. Numéro de notice : A2021-147 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10010039 Date de publication en ligne : 19/01/2021 En ligne : https://doi.org/10.3390/ijgi10010039 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97055
in ISPRS International journal of geo-information > vol 10 n° 1 (January 2021) . - n° 10[article]Automatic extraction of road intersection points from USGS historical map series using deep convolutional neural networks / Mahmoud Saeedimoghaddam in International journal of geographical information science IJGIS, vol 34 n° 5 (May 2020)
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Titre : Automatic extraction of road intersection points from USGS historical map series using deep convolutional neural networks Type de document : Article/Communication Auteurs : Mahmoud Saeedimoghaddam, Auteur ; Tomasz F. Stepinski, Auteur Année de publication : 2020 Article en page(s) : pp 947 - 968 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] carrefour
[Termes IGN] carte ancienne
[Termes IGN] carte numérisée
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'objet
[Termes IGN] données localisées
[Termes IGN] Etats-Unis
[Termes IGN] extraction du réseau routier
[Termes IGN] image RVB
[Termes IGN] numérisation automatique
[Termes IGN] représentation cartographique
[Termes IGN] système d'information géographique
[Termes IGN] vision par ordinateurRésumé : (auteur) Road intersection data have been used across a range of geospatial analyses. However, many datasets dating from before the advent of GIS are only available as historical printed maps. To be analyzed by GIS software, they need to be scanned and transformed into a usable (vector-based) format. Because the number of scanned historical maps is voluminous, automated methods of digitization and transformation are needed. Frequently, these processes are based on computer vision algorithms. However, the key challenges to this are (1) the low conversion accuracy for low quality and visually complex maps, and (2) the selection of optimal parameters. In this paper, we used a region-based deep convolutional neural network-based framework (RCNN) for object detection, in order to automatically identify road intersections in historical maps of several cities in the United States of America. We found that the RCNN approach is more accurate than traditional computer vision algorithms for double-line cartographic representation of the roads, though its accuracy does not surpass all traditional methods used for single-line symbols. The results suggest that the number of errors in the outputs is sensitive to complexity and blurriness of the maps, and to the number of distinct red-green-blue (RGB) combinations within them. Numéro de notice : A2020-205 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1696968 Date de publication en ligne : 28/11/2019 En ligne : https://doi.org/10.1080/13658816.2019.1696968 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94882
in International journal of geographical information science IJGIS > vol 34 n° 5 (May 2020) . - pp 947 - 968[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2020051 RAB Revue Centre de documentation En réserve L003 Disponible Edge-reinforced convolutional neural network for road detection in very-high-resolution remote sensing imagery / Xiaoyan Lu in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 3 (March 2020)
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Titre : Edge-reinforced convolutional neural network for road detection in very-high-resolution remote sensing imagery Type de document : Article/Communication Auteurs : Xiaoyan Lu, Auteur ; Yanfei Zhong, Auteur ; Zhuo Zheng, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 153 - 160 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] accentuation de contours
[Termes IGN] analyse multiéchelle
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] extraction du réseau routier
[Termes IGN] filtrage du bruit
[Termes IGN] image à très haute résolution
[Termes IGN] ombre
[Termes IGN] segmentation d'imageRésumé : (auteur) Road detection in very-high-resolution remote sensing imagery is a hot research topic. However, the high resolution results in highly complex data distributions, which lead to much noise for road detection—for example, shadows and occlusions caused by disturbance on the roadside make it difficult to accurately recognize road. In this article, a novel edge-reinforced convolutional neural network, combined with multiscale feature extraction and edge reinforcement, is proposed to alleviate this problem. First, multiscale feature extraction is used in the center part of the proposed network to extract multiscale context information. Then edge reinforcement, applying a simplified U-Net to learn additional edge information, is used to restore the road information. The two operations can be used with different convolutional neural networks. Finally, two public road data sets are adopted to verify the effectiveness of the proposed approach, with experimental results demonstrating its superiority. Numéro de notice : A2020-145 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.3.153 Date de publication en ligne : 01/03/2020 En ligne : https://doi.org/10.14358/PERS.86.3.153 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94774
in Photogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 3 (March 2020) . - pp 153 - 160[article]Automated extraction of lane markings from mobile LiDAR point clouds based on fuzzy inference / Heidar Rastiveis in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)
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Titre : Automated extraction of lane markings from mobile LiDAR point clouds based on fuzzy inference Type de document : Article/Communication Auteurs : Heidar Rastiveis, Auteur ; Alireza Shams, Auteur ; Wayne A. Sarasua, Auteur ; Jonathan Li, Auteur Année de publication : 2020 Article en page(s) : pp 149 - 166 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] autoroute
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction automatique
[Termes IGN] extraction de points
[Termes IGN] extraction du réseau routier
[Termes IGN] Inférence floue
[Termes IGN] lidar mobile
[Termes IGN] modélisation 3D
[Termes IGN] semis de points
[Termes IGN] transformation de HoughRésumé : (Auteur) Mobile LiDAR systems (MLS) are rapid and accurate technologies for acquiring three-dimensional (3D) point clouds that can be used to generate 3D models of road environments. Because manual extraction of desirable features such as road traffic signs, trees, and pavement markings from these point clouds is tedious and time-consuming, automatic information extraction of these objects is desirable. This paper proposes a novel automatic method to extract pavement lane markings (LMs) using point attributes associated with the MLS point cloud based on fuzzy inference. The proposed method begins with dividing the MLS point cloud into a number of small sections (e.g. tiles) along the route. After initial filtering of non-ground points, each section is vertically aligned. Next, a number of candidate LM areas are detected using a Hough Transform (HT) algorithm and considering a buffer area around each line. The points inside each area are divided into “probable-LM” and “non-LM” clusters. After extracting geometric and radiometric descriptors for the “probable-LM” clusters and analyzing them in a fuzzy inference system, true-LM clusters are eventually detected. Finally, the extracted points are enhanced and transformed back to their original position. The efficiency of the method was tested on two different point cloud datasets along 15.6 km and 9.5 km roadway corridors. Comparing the LMs extracted using the algorithm with the manually extracted LMs, 88% of the LM lines were successfully extracted in both datasets. Numéro de notice : A2020-047 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.12.009 Date de publication en ligne : 20/12/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.12.009 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94558
in ISPRS Journal of photogrammetry and remote sensing > vol 160 (February 2020) . - pp 149 - 166[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2020021 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020023 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020022 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt A cognitive framework for road detection from high-resolution satellite images / Naveen Chandra in Geocarto international, vol 34 n° 8 ([15/06/2019])PermalinkMultilane roads extracted from the OpenStreetMap urban road network using random forests / Yongyang Xu in Transactions in GIS, vol 23 n° 2 (April 2019)PermalinkIntegration of lidar data and GIS data for point cloud semantic enrichment at the point level / Harith Aljumaily in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 1 (January 2019)PermalinkSimultaneous chain-forming and generalization of road networks / Susanne Wenzel in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 1 (January 2019)PermalinkRoad safety evaluation through automatic extraction of road horizontal alignments from Mobile LiDAR System and inductive reasoning based on a decision tree / José Antonio Martin-Jimenez in ISPRS Journal of photogrammetry and remote sensing, vol 146 (December 2018)PermalinkNovel fusion approach on automatic object extraction from spatial data: case study Worldview-2 and TOPO5000 / Umut Gunes Sefercik in Geocarto international, vol 33 n° 10 (October 2018)PermalinkGenerative street addresses from satellite imagery / İlke Demir in ISPRS International journal of geo-information, vol 7 n° 3 (March 2018)Permalink3D local feature BKD to extract road information from mobile laser scanning point clouds / Yang Bisheng in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)PermalinkSimultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks / Rasha Alshehhi in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)PermalinkLarge-scale road detection in forested mountainous areas using airborne topographic lidar data / António Ferraz in ISPRS Journal of photogrammetry and remote sensing, vol 112 (February 2016)Permalink