Descripteur
Documents disponibles dans cette catégorie (2201)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Adversarial defenses for object detectors based on Gabor convolutional layers / Abdollah Amirkhani in The Visual Computer, vol 38 n° 6 (June 2022)
[article]
Titre : Adversarial defenses for object detectors based on Gabor convolutional layers Type de document : Article/Communication Auteurs : Abdollah Amirkhani, Auteur ; Mohammad Karimi, Auteur Année de publication : 2022 Article en page(s) : pp 1929 - 1944 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] détection d'objet
[Termes IGN] filtre de Gabor
[Termes IGN] reconnaissance de formes
[Termes IGN] réseau antagoniste génératifRésumé : (auteur) Despite their many advantages and positive features, the deep neural networks are extremely vulnerable against adversarial attacks. This drawback has substantially reduced the adversarial accuracy of the visual object detectors. To make these object detectors robust to adversarial attacks, a new Gabor filter-based method has been proposed in this paper. This method has then been applied on the YOLOv3 with different backbones, the SSD with different input sizes and on the FRCNN; and thus, six robust object detector models have been presented. In order to evaluate the efficacy of the models, they have been subjected to adversarial training via three types of targeted attacks (TOG-fabrication, TOG-vanishing, and TOG-mislabeling) and three types of untargeted random attacks (DAG, RAP, and UEA). The best average accuracy (49.6%) was achieved by the YOLOv3-d model, and for the PASCAL VOC dataset. This is far superior to the best performance and accuracy and obtained in previous works (25.4%). Empirical results show that, while the presented approach improves the adversarial accuracy of the object detector models, it does not affect the performance of these models on clean data. Numéro de notice : A2022-382 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-021-02256-6 Date de publication en ligne : 24/07/2021 En ligne : https://doi.org/10.1007/s00371-021-02256-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100651
in The Visual Computer > vol 38 n° 6 (June 2022) . - pp 1929 - 1944[article]Artificial intelligence techniques in extracting building and tree footprints using aerial imagery and LiDAR data / Saeideh Sahebi Vayghan in Geocarto international, vol 37 n° 10 ([01/06/2022])
[article]
Titre : Artificial intelligence techniques in extracting building and tree footprints using aerial imagery and LiDAR data Type de document : Article/Communication Auteurs : Saeideh Sahebi Vayghan, Auteur ; Mohammad Salmani, Auteur ; Neda Ghasemkhanic, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2967 - 2995 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] algorithme génétique
[Termes IGN] classification par nuées dynamiques
[Termes IGN] classification par réseau neuronal
[Termes IGN] détection d'arbres
[Termes IGN] détection du bâti
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] empreinte
[Termes IGN] image aérienne
[Termes IGN] image optique
[Termes IGN] Inférence floue
[Termes IGN] morphologie mathématiqueRésumé : (auteur) One of the most important considerations in urban environments is the extraction of urban objects, with a high automation level. This study aims to present a new method which uses aerial images and LiDAR data to extract buildings and trees footprint in urban areas. In this study, high-elevation objects were extracted from the LiDAR data using the developed scan labeling method, and then the classification methods of Neural Networks (NN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Genetic Based K-Means algorithm (GBKMs) were used to separate buildings and trees and with the purpose of evaluating their performance. The features used for the classification were extracted from aerial images and LiDAR data, and the training data for the classification were selected automatically. Mathematical morphology functions were also used to process the classification results. The results show that NN and the ANFIS are more effective than the genetic-based K-Means algorithm in detecting small and large buildings. Numéro de notice : A2022-596 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1844311 En ligne : https://doi.org/10.1080/10106049.2020.1844311 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101300
in Geocarto international > vol 37 n° 10 [01/06/2022] . - pp 2967 - 2995[article]Context-aware network for semantic segmentation toward large-scale point clouds in urban environments / Chun Liu in IEEE Transactions on geoscience and remote sensing, vol 60 n° 6 (June 2022)
[article]
Titre : Context-aware network for semantic segmentation toward large-scale point clouds in urban environments Type de document : Article/Communication Auteurs : Chun Liu, Auteur ; Doudou Zeng, Auteur ; Akram Akbar, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 5703915 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] agrégation de détails
[Termes IGN] apprentissage profond
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] graphe
[Termes IGN] prise en compte du contexte
[Termes IGN] segmentation sémantique
[Termes IGN] semis de points
[Termes IGN] zone urbaineRésumé : (auteur) Point cloud semantic segmentation in urban scenes plays a vital role in intelligent city modeling, autonomous driving, and urban planning. Point cloud semantic segmentation based on deep learning methods has achieved significant improvement. However, it is also challenging for accurate semantic segmentation in large scenes due to complex elements, variety of scene classes, occlusions, and noise. Besides, most methods need to split the original point cloud into multiple blocks before processing and cannot directly deal with the point clouds on a large scale. We propose a novel context-aware network (CAN) that can directly deal with large-scale point clouds. In the proposed network, a local feature aggregation module (LFAM) is designed to preserve rich geometric details in the raw point cloud and reduce the information loss during feature extraction. Then, in combination with a global context aggregation module (GCAM), capture long-range dependencies to enhance the network feature representation and suppress the noise. Finally, a context-aware upsampling module (CAUM) is embedded into the proposed network to capture the global perception from a broad perspective. The ensemble of low-level and high-level features facilitates the effectiveness and efficiency of 3-D point cloud feature refinement. Comprehensive experiments were carried out on three large-scale point cloud datasets in both outdoor and indoor environments to evaluate the performance of the proposed network. The results show that the proposed method outperformed the state-of-the-art representative semantic segmentation networks, and the overall accuracy (OA) of Tongji-3D, Semantic3D, and Stanford large-scale 3-D indoor spaces (S3DIS) is 96.01%, 95.0%, and 88.55%, respectively. Numéro de notice : A2022-561 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3182776 Date de publication en ligne : 13/06/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3182776 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101188
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 6 (June 2022) . - n° 5703915[article]Detecting interchanges in road networks using a graph convolutional network approach / Min Yang in International journal of geographical information science IJGIS, vol 36 n° 6 (June 2022)
[article]
Titre : Detecting interchanges in road networks using a graph convolutional network approach Type de document : Article/Communication Auteurs : Min Yang, Auteur ; Chenjun Jiang, Auteur ; Xiongfeng Yan, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1119 - 1139 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de groupement
[Termes IGN] analyse vectorielle
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification semi-dirigée
[Termes IGN] détection d'objet
[Termes IGN] échangeur routier
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] modélisation
[Termes IGN] noeud
[Termes IGN] Pékin (Chine)
[Termes IGN] réseau neuronal de graphes
[Termes IGN] réseau routier
[Termes IGN] Wuhan (Chine)Résumé : (auteur) Detecting interchanges in road networks benefit many applications, such as vehicle navigation and map generalization. Traditional approaches use manually defined rules based on geometric, topological, or both properties, and thus can present challenges for structurally complex interchange. To overcome this drawback, we propose a graph-based deep learning approach for interchange detection. First, we model the road network as a graph in which the nodes represent road segments, and the edges represent their connections. The proposed approach computes the shape measures and contextual properties of individual road segments for features characterizing the associated nodes in the graph. Next, a semi-supervised approach uses these features and limited labeled interchanges to train a graph convolutional network that classifies these road segments into an interchange and non-interchange segments. Finally, an adaptive clustering approach groups the detected interchange segments into interchanges. Our experiment with the road networks of Beijing and Wuhan achieved a classification accuracy >95% at a label rate of 10%. Moreover, the interchange detection precision and recall were 79.6 and 75.7% on the Beijing dataset and 80.6 and 74.8% on the Wuhan dataset, respectively, which were 18.3–36.1 and 17.4–19.4% higher than those of the existing approaches based on characteristic node clustering. Numéro de notice : A2022-404 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.2024195 Date de publication en ligne : 11/03/2022 En ligne : https://doi.org/10.1080/13658816.2021.2024195 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100716
in International journal of geographical information science IJGIS > vol 36 n° 6 (June 2022) . - pp 1119 - 1139[article]Évaluation de la qualité de modèles 3D issus de nuages de points / Tania Landes in XYZ, n° 171 (juin 2022)
[article]
Titre : Évaluation de la qualité de modèles 3D issus de nuages de points Type de document : Article/Communication Auteurs : Tania Landes, Auteur Année de publication : 2022 Article en page(s) : pp 14 - 24 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] maquette numérique
[Termes IGN] modélisation 3D du bâti BIM
[Termes IGN] précision géométrique (imagerie)
[Termes IGN] qualité du modèle
[Termes IGN] reconstruction 3D du bâti
[Termes IGN] segmentation d'image
[Termes IGN] semis de pointsRésumé : (Auteur) La modélisation 3D répond à la fois à un enjeu économique, mais aussi environnemental, que ce soit à l’échelle du bâtiment ou de la ville. Ces dix dernières années, les techniques d’acquisition ont considérablement évolué du point de vue de leur rapidité, du volume de données à gérer, de l’hétérogénéité des informations acquises par les systèmes multicapteurs, de même que des méthodes de traitement des données. De nouveaux processus sont nés de ces bouleversements, comme le processus “scan-to-BIM”, caractérisant les étapes menant du nuage de points à une maquette numérique intelligente. En adoptant la maquette numérique, intégrée dans un processus collaboratif BIM (Building Information Modeling), les acteurs du bâtiment sont en mesure d’effectuer des simulations et de réduire, en plus des coûts, l’impact environnemental lié aux interventions sur le bâtiment, tout au long de son cycle de vie. En pratique, pour aboutir à une maquette numérique intelligente du bâtiment à partir d’un relevé de l’existant, de nombreux verrous technologiques sont à lever. Dans ce contexte, j’ai eu l’occasion d’encadrer divers travaux de recherches portant sur les thématiques allant de l’acquisition de données 3D (généralement sous forme de nuages de points 3D) à leur traitement, jusqu’à la production de la maquette numérique comme résumé dans le numéro 167 de la revue XYZ [Landes, 2021]. Dans la continuité de ce résumé, et comme l’annonçait la conclusion de ce dernier, cette suite se concentre sur la question de la qualité des livrables 3D détaillés dans [Landes, 2020]. Numéro de notice : A2022-521 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtSansCL DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101066
in XYZ > n° 171 (juin 2022) . - pp 14 - 24[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 112-2022021 RAB Revue Centre de documentation En réserve L003 Disponible Extracting the urban landscape features of the historic district from street view images based on deep learning: A case study in the Beijing Core area / Siming Yin in ISPRS International journal of geo-information, vol 11 n° 6 (June 2022)PermalinkFeature-selection high-resolution network with hypersphere embedding for semantic segmentation of VHR remote sensing images / Hanwen Xu in IEEE Transactions on geoscience and remote sensing, vol 60 n° 6 (June 2022)PermalinkGraph-based block-level urban change detection using Sentinel-2 time series / Nan Wang in Remote sensing of environment, vol 274 (June 2022)PermalinkInvariant structure representation for remote sensing object detection based on graph modeling / Zicong Zhu in IEEE Transactions on geoscience and remote sensing, vol 60 n° 6 (June 2022)PermalinkLarge-scale automatic identification of urban vacant land using semantic segmentation of high-resolution remote sensing images / Lingdong Mao in Landscape and Urban Planning, vol 222 (June 2022)PermalinkLine-based deep learning method for tree branch detection from digital images / Rodrigo L. S. Silva in International journal of applied Earth observation and geoinformation, vol 110 (June 2022)PermalinkPrecise crop classification of hyperspectral images using multi-branch feature fusion and dilation-based MLP / Haibin Wu in Remote sensing, vol 14 n° 11 (June-1 2022)PermalinkRecent advances in forest insect pests and diseases monitoring using UAV-based data: A systematic review / André Duarte in Forests, vol 13 n° 6 (June 2022)PermalinkThe interrelationship between LST, NDVI, NDBI, and land cover change in a section of Lagos metropolis, Nigeria / Alfred S. Alademomi in Applied geomatics, vol 14 n° 2 (June 2022)PermalinkThe promising combination of a remote sensing approach and landscape connectivity modelling at a fine scale in urban planning / Elie Morin in Ecological indicators, vol 139 (June 2022)PermalinkTowards the automated large-scale reconstruction of past road networks from historical maps / Johannes H. Uhl in Computers, Environment and Urban Systems, vol 94 (June 2022)PermalinkTrue orthophoto generation based on unmanned aerial vehicle images using reconstructed edge points / Mojdeh Ebrahimikia in Photogrammetric record, vol 37 n° 178 (June 2022)PermalinkAn informal road detection neural network for societal impact in developing countries / Inger Fabris-Rotelli in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-4-2022 (2022 edition)PermalinkApplication oriented quality evaluation of Gaofen-7 optical stereo satellite imagery / Jiaojiao Tian in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-1-2022 (2022 edition)PermalinkCliff change detection using siamese KPCONV deep network on 3D point clouds / Iris de Gelis in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-3-2022 (2022 edition)PermalinkK-means clustering based on omnivariance attribute for building detection from airborne lidar data / Renato César Dos santos in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)PermalinkVirtual laser scanning of dynamic scenes created from real 4D topographic point cloud data / Lukas Winiwarter in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)PermalinkDetection and mapping of snow avalanche debris from Western Himalaya, India using remote sensing satellite images / Kamal Kant Singh in Geocarto international, vol 37 n° 9 ([15/05/2022])PermalinkA new method to detect targets in hyperspectral images based on principal component analysis / Shahram Sharifi Hashjin in Geocarto international, vol 37 n° 9 ([15/05/2022])PermalinkResearch on automatic identification method of terraces on the Loess plateau based on deep transfer learning / Mingge Yu in Remote sensing, vol 14 n° 10 (May-2 2022)Permalink