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Machine learning in ground motion prediction / Farid Khosravikia in Computers & geosciences, vol 148 (March 2021)
[article]
Titre : Machine learning in ground motion prediction Type de document : Article/Communication Auteurs : Farid Khosravikia, Auteur ; Patricia Clayton, Auteur Année de publication : 2021 Article en page(s) : n° 104700 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] apprentissage automatique
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] Etats-Unis
[Termes IGN] modèle de régression
[Termes IGN] modèle de simulation
[Termes IGN] mouvement de terrain
[Termes IGN] régression linéaire
[Termes IGN] réseau neuronal artificiel
[Termes IGN] sismicitéRésumé : (auteur) This paper studies the advantages and disadvantages of different machine learning techniques in predicting ground-motion intensity measures given source characteristics, source-to-site distance, and local site conditions. Typically, linear regression-based models with predefined equations and coefficients are used in ground motion prediction. However, restrictions of the linear regression models may limit their capabilities in extracting complex nonlinear behaviors in the data. Therefore, the present paper comparatively investigates potential benefits from employing other machine learning techniques as statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. This study quantifies event-to-event and site-to-site variability of the ground motions by implementing them as random effect terms to reduce the aleatory uncertainty. All the algorithms are trained using a selected database of 4528 ground-motions, including 376 seismic events with magnitude 3 to 5.8, recorded over the hypocentral distance range of 4–500 km in Oklahoma, Kansas, and Texas since 2005. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring predefined equations or coefficients. Moreover, it is found that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available. Numéro de notice : A2021-230 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/POSITIONNEMENT Nature : Article DOI : 10.1016/j.cageo.2021.104700 Date de publication en ligne : 21/01/2021 En ligne : https://doi.org/10.1016/j.cageo.2021.104700 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97220
in Computers & geosciences > vol 148 (March 2021) . - n° 104700[article]Multi-level progressive parallel attention guided salient object detection for RGB-D images / Zhengyi Liu in The Visual Computer, vol 37 n° 3 (March 2021)
[article]
Titre : Multi-level progressive parallel attention guided salient object detection for RGB-D images Type de document : Article/Communication Auteurs : Zhengyi Liu, Auteur ; Quntao Duan, Auteur ; Song Shi, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 529 - 540 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] approche hiérarchique
[Termes IGN] détection automatique
[Termes IGN] détection d'objet
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image RVB
[Termes IGN] optimisation spatiale
[Termes IGN] profondeur
[Termes IGN] réseau neuronal récurrent
[Termes IGN] saillanceRésumé : (auteur) Detecting salient objects in RGB-D images attracts more and more attention in recent years. It benefits from the widespread use of depth sensors and can be applied in the comprehensive understanding of RGB-D images. Existing models focus on double-stream networks which transfer from color stream to depth stream, but depth stream with one channel information cannot learn the same feature as color stream with three channels information even if HHA representation is adopted. In our works, RGB-D four-channels input is chosen, and meanwhile, progressive parallel spatial and channel attention mechanisms are performed to improve feature representation. Spatial and channel attention can pay more attention on partial positions and channels in the image which show higher response to salient objects. Both attentive features are optimized by attentive feature from higher layer, respectively, and parallel fed into recurrent convolutional layer to generate side-output saliency maps guided by saliency map from higher layer. Last multi-level saliency maps are fused together from multi-scale perspective. Experiments on benchmark datasets demonstrate that parallel attention mechanism and progressive optimization operation play an important role in improving the accuracy of salient object detection, and our model outperforms state-of-the-art models in evaluation matrices. Numéro de notice : A2021-340 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-020-01821-9 Date de publication en ligne : 18/02/2020 En ligne : https://doi.org/10.1007/s00371-020-01821-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97578
in The Visual Computer > vol 37 n° 3 (March 2021) . - pp 529 - 540[article]PBNet: Part-based convolutional neural network for complex composite object detection in remote sensing imagery / Xian Sun in ISPRS Journal of photogrammetry and remote sensing, vol 173 (March 2021)
[article]
Titre : PBNet: Part-based convolutional neural network for complex composite object detection in remote sensing imagery Type de document : Article/Communication Auteurs : Xian Sun, Auteur ; Peijin Wang, Auteur ; Cheng Wang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 50 - 65 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage profond
[Termes IGN] Chine
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'objet
[Termes IGN] objet géographique complexe
[Termes IGN] prise en compte du contexte
[Termes IGN] rectangle englobant minimumRésumé : (auteur) In recent years, deep learning-based algorithms have brought great improvements to rigid object detection. In addition to rigid objects, remote sensing images also contain many complex composite objects, such as sewage treatment plants, golf courses, and airports, which have neither a fixed shape nor a fixed size. In this paper, we validate through experiments that the results of existing methods in detecting composite objects are not satisfying enough. Therefore, we propose a unified part-based convolutional neural network (PBNet), which is specifically designed for composite object detection in remote sensing imagery. PBNet treats a composite object as a group of parts and incorporates part information into context information to improve composite object detection. Correct part information can guide the prediction of a composite object, thus solving the problems caused by various shapes and sizes. To generate accurate part information, we design a part localization module to learn the classification and localization of part points using bounding box annotation only. A context refinement module is designed to generate more discriminative features by aggregating local context information and global context information, which enhances the learning of part information and improve the ability of feature representation. We selected three typical categories of composite objects from a public dataset to conduct experiments to verify the detection performance and generalization ability of our method. Meanwhile, we build a more challenging dataset about a typical kind of complex composite objects, i.e., sewage treatment plants. It refers to the relevant information from authorities and experts. This dataset contains sewage treatment plants in seven cities in the Yangtze valley, covering a wide range of regions. Comprehensive experiments on two datasets show that PBNet surpasses the existing detection algorithms and achieves state-of-the-art accuracy. Numéro de notice : A2021-105 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.12.015 Date de publication en ligne : 16/01/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.12.015 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96891
in ISPRS Journal of photogrammetry and remote sensing > vol 173 (March 2021) . - pp 50 - 65[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2021031 SL Revue Centre de documentation Revues en salle Disponible 081-2021033 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2021032 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Recognition of varying size scene images using semantic analysis of deep activation maps / Shikha Gupta in Machine Vision and Applications, vol 32 n° 2 (March 2021)
[article]
Titre : Recognition of varying size scene images using semantic analysis of deep activation maps Type de document : Article/Communication Auteurs : Shikha Gupta, Auteur ; A.D. Dileep, Auteur ; Veena Thenkanidiyoor, Auteur Année de publication : 2021 Article en page(s) : n° 52 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de groupement
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] compréhension de l'image
[Termes IGN] méthode fondée sur le noyau
[Termes IGN] modèle conceptuel de données
[Termes IGN] reconnaissance de formes
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Understanding the complex semantic structure of scene images requires mapping the image from pixel space to high-level semantic space. In semantic space, a scene image is represented by the posterior probabilities of concepts (e.g., ‘car,’ ‘chair,’ ‘window,’ etc.) present in it and such representation is known as semantic multinomial (SMN) representation. SMN generation requires a concept annotated dataset for concept modeling which is infeasible to generate manually due to the large size of databases. To tackle this issue, we propose a novel approach of building the concept model via pseudo-concepts. Pseudo-concept acts as a proxy for the actual concept and gives the cue for its presence instead of actual identity. We propose to use filter responses from deeper convolutional layers of convolutional neural networks (CNNs) as pseudo-concepts, as filters in deeper convolutional layers are trained for different semantic concepts. Most of the prior work considers fixed-size (≈227×227) images for semantic analysis which suppresses many concepts present in the images. In this work, we preserve the true-concept structure in images by passing in their original resolution to convolutional layers of CNNs. We further propose to prune the non-prominent pseudo-concepts, group the similar one using kernel clustering and later model them using a dynamic-based support vector machine. We demonstrate that resulting SMN representation indeed captures the semantic concepts better and results in state-of-the-art classification accuracy on varying size scene image datasets such as MIT67 and SUN397. Numéro de notice : A2021-454 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00138-021-01168-8 Date de publication en ligne : 01/03/2021 En ligne : https://doi.org/10.1007/s00138-021-01168-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97898
in Machine Vision and Applications > vol 32 n° 2 (March 2021) . - n° 52[article]Robust unsupervised small area change detection from SAR imagery using deep learning / Xinzheng Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 173 (March 2021)
[article]
Titre : Robust unsupervised small area change detection from SAR imagery using deep learning Type de document : Article/Communication Auteurs : Xinzheng Zhang, Auteur ; Hang Su, Auteur ; Ce Zhang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 79 - 94 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] apprentissage profond
[Termes IGN] classification floue
[Termes IGN] classification non dirigée
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection de changement
[Termes IGN] échantillonnage
[Termes IGN] filtre de déchatoiement
[Termes IGN] image radar moirée
[Termes IGN] ondelette
[Termes IGN] regroupement de données
[Termes IGN] superpixelRésumé : (auteur) Small area change detection using synthetic aperture radar (SAR) imagery is a highly challenging task, due to speckle noise and imbalance between classes (changed and unchanged). In this paper, a robust unsupervised approach is proposed for small area change detection using deep learning techniques. First, a multi-scale superpixel reconstruction method is developed to generate a difference image (DI), which can suppress the speckle noise effectively and enhance edges by exploiting local, spatially homogeneous information. Second, a two-stage centre-constrained fuzzy c-means clustering algorithm is proposed to divide the pixels of the DI into changed, unchanged and intermediate classes with a parallel clustering strategy. Image patches belonging to the first two classes are then constructed as pseudo-label training samples, and image patches of the intermediate class are treated as testing samples. Finally, a convolutional wavelet neural network (CWNN) is designed and trained to classify testing samples into changed or unchanged classes, coupled with a deep convolutional generative adversarial network (DCGAN) to increase the number of changed class within the pseudo-label training samples. Numerical experiments on four real SAR datasets demonstrate the validity and robustness of the proposed approach, achieving up to 99.61% accuracy for small area change detection. Numéro de notice : A2021-103 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.01.004 Date de publication en ligne : 17/01/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.01.004 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96879
in ISPRS Journal of photogrammetry and remote sensing > vol 173 (March 2021) . - pp 79 - 94[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2021031 SL Revue Centre de documentation Revues en salle Disponible 081-2021033 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2021032 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Suitability assessment of urban land use in Dalian, China using PNN and GIS / Ziqian Kang in Natural Hazards, vol 106 n° 1 (March 2021)PermalinkToward a yearly country-scale CORINE land-cover map without using images: A map translation approach / Luc Baudoux in Remote sensing, Vol 13 n° 6 (March 2021)PermalinkUrban flood hazard mapping using machine learning models: GARP, RF, MaxEnt and NB / Mahya Norallahi in Natural Hazards, vol 106 n° 1 (March 2021)PermalinkCoastal water remote sensing from sentinel-2 satellite data using physical, statistical, and neural network retrieval approach / Frank S. Marzano in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)PermalinkA comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping / Zhice Fang in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)PermalinkCrop identification by massive processing of multiannual satellite imagery for EU common agriculture policy subsidy control / Adolfo Lozano-Tello in European journal of remote sensing, vol 54 n° 1 (2021)PermalinkDeep traffic light detection by overlaying synthetic context on arbitrary natural images / Jean Pablo Vieira de Mello in Computers and graphics, vol 94 n° 1 (February 2021)PermalinkGeographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling / Stefanos Georganos in Geocarto international, vol 36 n° 2 ([01/02/2021])PermalinkGTP-PNet: A residual learning network based on gradient transformation prior for pansharpening / Hao Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 172 (February 2021)PermalinkMultiscale CNN with autoencoder regularization joint contextual attention network for SAR image classification / Zitong Wu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)PermalinkA points of interest matching method using a multivariate weighting function with gradient descent optimization / Zhou Yang in Transactions in GIS, Vol 25 n° 1 (February 2021)PermalinkRoom semantics inference using random forest and relational graph convolutional networks: A case study of research building / Xuke Hu in Transactions in GIS, Vol 25 n° 1 (February 2021)PermalinkSemi-supervised joint learning for hand gesture recognition from a single color image / Chi Xu in Sensors, vol 21 n° 3 (February 2021)PermalinkTropical forest canopy height estimation from combined polarimetric SAR and LiDAR using machine-learning / Maryam Pourshamsi in ISPRS Journal of photogrammetry and remote sensing, vol 172 (February 2021)PermalinkUnsupervised deep representation learning for real-time tracking / Ning Wang in International journal of computer vision, vol 129 n° 2 (February 2021)PermalinkMapping seasonal agricultural land use types using deep learning on Sentinel-2 image time series / Misganu Debella-Gilo in Remote sensing, Vol 13 n° 2 (January-2 2021)PermalinkPermalink3D urban scene understanding by analysis of LiDAR, color and hyperspectral data / David Duque-Arias (2021)PermalinkPermalinkAleatoric uncertainty estimation for dense stereo matching via CNN-based cost volume analysis / Max Mehltretter in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)PermalinkAmélioration des résolutions spatiale et spectrale d’images satellitaires par réseaux antagonistes / Anaïs Gastineau (2021)PermalinkAn efficient representation of 3D buildings: application to the evaluation of city models / Oussama Ennafii (2021)PermalinkPermalinkPermalinkPermalinkApplications of remote sensing data in mapping of forest growing stock and biomass / Jose Aranha (2021)PermalinkApport de la photogrammétrie et de l’intelligence artificielle à la détection des zones amiantées sur les fronts rocheux / Philippe Caudal (2021)PermalinkApports des méthodes d'apprentissage profond pour la reconnaissance automatique des modes d'occupation des sols et d'objets par télédétection en milieu tropical / Guillaume Rousset (2021)PermalinkApprentissage profond et IA pour l’amélioration de la robustesse des techniques de localisation par vision artificielle / Achref Elouni (2021)PermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkClustering et apprentissage profond sous contraintes pour l’analyse de séries temporelles : Application à l’analyse temporelle incrémentale en télédétection / Baptiste Lafabregue (2021)PermalinkCombining deep learning and mathematical morphology for historical map segmentation / Yizi Chen (2021)PermalinkConnecting images through time and sources: Introducing low-data, heterogeneous instance retrieval / Dimitri Gominski (2021)PermalinkConsolidation of crowd-sourced geo-ragged data for parameterized travel recommendations / Ago Luberg (2021)PermalinkPermalinkContributions to graph-based hierarchical analysis for images and 3D point clouds / Leonardo Gigli (2021)PermalinkPermalink