Descripteur
Documents disponibles dans cette catégorie (525)
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]Assessing and mapping landslide susceptibility using different machine learning methods / Osman Orhan in Geocarto international, vol 37 n° 10 ([01/06/2022])
[article]
Titre : Assessing and mapping landslide susceptibility using different machine learning methods Type de document : Article/Communication Auteurs : Osman Orhan, Auteur ; Suleyman Sefa Bilgilioglu, Auteur ; Zehra Kaya, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2795 - 2820 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] apprentissage automatique
[Termes IGN] carte thématique
[Termes IGN] classification et arbre de régression
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] effondrement de terrain
[Termes IGN] lithologie
[Termes IGN] pente
[Termes IGN] régression logistique
[Termes IGN] réseau neuronal artificiel
[Termes IGN] séparateur à vaste marge
[Termes IGN] TurquieRésumé : (auteur) The main aim of the present study was to produce and compare landslide susceptibility maps by using five machine learning techniques, namely, artificial neural network (ANN), logistic regression (LR), support vector machine (SVM), random forest (RF) and, classification and regression tree (CART). The study area was determined as the Arhavi-Kabisre river basin, a region in which the most landslide incidents occur in Turkey. Firstly, a landslide inventory was produced by identifying a total of 252 landslides. Secondly, a total of 11 landslide conditioning factors were considered for the landslide susceptibility mapping. Subsequently, the five machine learning techniques were constructed with the help of the training dataset for the landslide susceptibility maps. Finally, the receiver operating characteristic (ROC), sensitivity, specificity, F-measure, accuracy and kappa index were applied to compare and validate the performance of the five machine learning techniques. Numéro de notice : A2022-594 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1837258 Date de publication en ligne : 30/10/2020 En ligne : https://doi.org/10.1080/10106049.2020.1837258 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101298
in Geocarto international > vol 37 n° 10 [01/06/2022] . - pp 2795 - 2820[article]Beyond single receptive field: A receptive field fusion-and-stratification network for airborne laser scanning point cloud classification / Yongqiang Mao in ISPRS Journal of photogrammetry and remote sensing, vol 188 (June 2022)
[article]
Titre : Beyond single receptive field: A receptive field fusion-and-stratification network for airborne laser scanning point cloud classification Type de document : Article/Communication Auteurs : Yongqiang Mao, Auteur ; Kaiqiang chen, Auteur ; Wenhui Diao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 45 - 61 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage automatique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données laser
[Termes IGN] données localisées 3D
[Termes IGN] Perceptron multicouche
[Termes IGN] représentation parcimonieuse
[Termes IGN] réseau neuronal de graphes
[Termes IGN] semis de points
[Termes IGN] stratification de données
[Termes IGN] voxelRésumé : (Auteur) The classification of airborne laser scanning (ALS) point clouds is a critical task of remote sensing and photogrammetry fields. Although recent deep learning-based methods have achieved satisfactory performance, they have ignored the unicity of the receptive field, which makes the ALS point cloud classification remain challenging for the distinguishment of the areas with complex structures and extreme scale variations. In this article, for the objective of configuring multi-receptive field features, we propose a novel receptive field fusion-and-stratification network (RFFS-Net). With a novel dilated graph convolution (DGConv) and its extension annular dilated convolution (ADConv) as basic building blocks, the receptive field fusion process is implemented with the dilated and annular graph fusion (DAGFusion) module, which obtains multi-receptive field feature representation through capturing dilated and annular graphs with various receptive regions. The stratification of the receptive fields with point sets of different resolutions as the calculation bases is performed with Multi-level Decoders nested in RFFS-Net and driven by the multi-level receptive field aggregation loss (MRFALoss) to drive the network to learn in the direction of the supervision labels with different resolutions. With receptive field fusion-and-stratification, RFFS-Net is more adaptable to the classification of regions with complex structures and extreme scale variations in large-scale ALS point clouds. Evaluated on the ISPRS Vaihingen 3D dataset, our RFFS-Net significantly outperforms the baseline (i.e. PointConv) approach by 5.3% on mF1 and 5.4% on mIoU, accomplishing an overall accuracy of 82.1%, an mF1 of 71.6%, and an mIoU of 58.2%. The experiments show that our RFFS-Net achieves a new state-of-the-art classification performance on powerline, car, and fence classes. Furthermore, experiments on the LASDU dataset and the 2019 IEEE-GRSS Data Fusion Contest dataset show that RFFS-Net achieves a new state-of-the-art classification performance. The code is available at github.com/WingkeungM/RFFS-Net. Numéro de notice : A2022-273 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.03.019 Date de publication en ligne : 07/04/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.03.019 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100532
in ISPRS Journal of photogrammetry and remote sensing > vol 188 (June 2022) . - pp 45 - 61[article]Réservation
Réserver ce documentExemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2022061 SL Revue Centre de documentation Revues en salle Disponible 081-2022063 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2022062 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Constraint-based evaluation of map images generalized by deep learning / Azelle Courtial in Journal of Geovisualization and Spatial Analysis, vol 6 n° 1 (June 2022)
[article]
Titre : Constraint-based evaluation of map images generalized by deep learning Type de document : Article/Communication Auteurs : Azelle Courtial , Auteur ; Guillaume Touya , Auteur ; Xiang Zhang, Auteur Année de publication : 2022 Projets : 2-Pas d'info accessible - article non ouvert / Article en page(s) : n° 13 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage profond
[Termes IGN] connexité (graphes)
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] montagne
[Termes IGN] programmation par contraintes
[Termes IGN] qualité des données
[Termes IGN] rendu réaliste
[Termes IGN] route
[Vedettes matières IGN] GénéralisationRésumé : (auteur) Deep learning techniques have recently been experimented for map generalization. Although promising, these experiments raise new problems regarding the evaluation of the output images. Traditional map generalization evaluation cannot directly be applied to the results in a raster format. Additionally, the internal evaluation used by deep learning models is mostly based on the realism of images and the accuracy of pixels, and none of these criteria is sufficient to evaluate a generalization process. Finally, deep learning processes tend to hide the causal mechanisms and do not always guarantee a result that follows cartographic principles. In this article, we propose a method to adapt constraint-based evaluation to the images generated by deep learning models. We focus on the use case of mountain road generalization, and detail seven raster-based constraints, namely, clutter, coalescence reduction, smoothness, position preservation, road connectivity preservation, noise absence, and color realism constraints. These constraints can contribute to current studies on deep learning-based map generalization, as they can help guide the learning process, compare different models, validate these models, and identify remaining problems in the output images. They can also be used to assess the quality of training examples. Numéro de notice : A2022-449 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s41651-022-00104-2 Date de publication en ligne : 07/05/2022 En ligne : http://dx.doi.org/10.1007/s41651-022-00104-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100646
in Journal of Geovisualization and Spatial Analysis > vol 6 n° 1 (June 2022) . - n° 13[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)PermalinkCoupling graph deep learning and spatial-temporal influence of built environment for short-term bus travel demand prediction / Tianhong Zhao in Computers, Environment and Urban Systems, vol 94 (June 2022)PermalinkDetecting 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)PermalinkDiffusionNet: discretization agnostic learning on surfaces / Nicholas Sharp in ACM Transactions on Graphics, TOG, Vol 41 n° 3 (June 2022)PermalinkExtracting 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)PermalinkGIS and machine learning for analysing influencing factors of bushfires using 40-year spatio-temporal bushfire data / Wanqin He in ISPRS International journal of geo-information, vol 11 n° 6 (June 2022)PermalinkHyperNet: A deep network for hyperspectral, multispectral, and panchromatic image fusion / Kun Li in ISPRS Journal of photogrammetry and remote sensing, vol 188 (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)Permalink