Descripteur
Termes IGN > informatique > intelligence artificielle > apprentissage automatique > apprentissage profond
apprentissage profond |
Documents disponibles dans cette catégorie (315)
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
Unsupervised pansharpening based on self-attention mechanism / Ying Qu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)
[article]
Titre : Unsupervised pansharpening based on self-attention mechanism Type de document : Article/Communication Auteurs : Ying Qu, Auteur ; Razieh Kaviani Baghbaderani, Auteur ; Hairong Qi, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 3192 - 3208 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] classification non dirigée
[Termes IGN] image multibande
[Termes IGN] pansharpening (fusion d'images)
[Termes IGN] pouvoir de résolution géométrique
[Termes IGN] précision infrapixellaire
[Termes IGN] reconstruction d'image
[Termes IGN] segmentation d'imageRésumé : (auteur) Pansharpening is to fuse a multispectral image (MSI) of low-spatial-resolution (LR) but rich spectral characteristics with a panchromatic image (PAN) of high spatial resolution (HR) but poor spectral characteristics. Traditional methods usually inject the extracted high-frequency details from PAN into the upsampled MSI. Recent deep learning endeavors are mostly supervised assuming that the HR MSI is available, which is unrealistic especially for satellite images. Nonetheless, these methods could not fully exploit the rich spectral characteristics in the MSI. Due to the wide existence of mixed pixels in satellite images where each pixel tends to cover more than one constituent material, pansharpening at the subpixel level becomes essential. In this article, we propose an unsupervised pansharpening (UP) method in a deep-learning framework to address the abovementioned challenges based on the self-attention mechanism (SAM), referred to as UP-SAM. The contribution of this article is threefold. First, the SAM is proposed where the spatial varying detail extraction and injection functions are estimated according to the attention representations indicating spectral characteristics of the MSI with subpixel accuracy. Second, such attention representations are derived from mixed pixels with the proposed stacked attention network powered with a stick-breaking structure to meet the physical constraints of mixed pixel formulations. Third, the detail extraction and injection functions are spatial varying based on the attention representations, which largely improves the reconstruction accuracy. Extensive experimental results demonstrate that the proposed approach is able to reconstruct sharper MSI of different types, with more details and less spectral distortion compared with the state-of-the-art. Numéro de notice : A2021-285 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3009207 Date de publication en ligne : 23/07/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3009207 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97394
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 4 (April 2021) . - pp 3192 - 3208[article]Visual positioning in indoor environments using RGB-D images and improved vector of local aggregated descriptors / Longyu Zhang in ISPRS International journal of geo-information, vol 10 n° 4 (April 2021)
[article]
Titre : Visual positioning in indoor environments using RGB-D images and improved vector of local aggregated descriptors Type de document : Article/Communication Auteurs : Longyu Zhang, Auteur ; Hao Xia, Auteur ; Qingjun Liu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 195 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification par nuées dynamiques
[Termes IGN] estimation de pose
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image RVB
[Termes IGN] modélisation 3D
[Termes IGN] positionnement en intérieur
[Termes IGN] Ransac (algorithme)
[Termes IGN] scène intérieure
[Termes IGN] semis de points
[Termes IGN] SIFT (algorithme)
[Termes IGN] SURF (algorithme)
[Termes IGN] téléphone intelligent
[Termes IGN] vision par ordinateurRésumé : (auteur) Positioning information has become one of the most important information for processing and displaying on smart mobile devices. In this paper, we propose a visual positioning method using RGB-D image on smart mobile devices. Firstly, the pose of each image in the training set is calculated through feature extraction and description, image registration, and pose map optimization. Then, in the image retrieval stage, the training set and the query set are clustered to generate the vector of local aggregated descriptors (VLAD) description vector. In order to overcome the problem that the description vector loses the image color information and improve the retrieval accuracy under different lighting conditions, the opponent color information and depth information are added to the description vector for retrieval. Finally, using the point cloud corresponding to the retrieval result image and its pose, the pose of the retrieved image is calculated by perspective-n-point (PnP) method. The results of indoor scene positioning under different illumination conditions show that the proposed method not only improves the positioning accuracy compared with the original VLAD and ORB-SLAM2, but also has high computational efficiency. Numéro de notice : A2021-481 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10040195 Date de publication en ligne : 24/03/2021 En ligne : https://doi.org/10.3390/ijgi10040195 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97425
in ISPRS International journal of geo-information > vol 10 n° 4 (April 2021) . - n° 195[article]Application of a multi-layer artificial neural network in a 3-D global electron density model using the long-term observations of COSMIC, Fengyun-3C, and Digisonde / Li Wang in Space weather, vol 19 n° 3 (March 2021)
[article]
Titre : Application of a multi-layer artificial neural network in a 3-D global electron density model using the long-term observations of COSMIC, Fengyun-3C, and Digisonde Type de document : Article/Communication Auteurs : Li Wang, Auteur ; Zhao Dongsheng ; Changyong He , Auteur ; et al., Auteur Année de publication : 2021 Projets : 3-projet - voir note / Article en page(s) : n° e2020SW002605 Note générale : bibliographie
The authors greatly appreciate the financial support from the National Natural Science Foundations of China (Grant No. 41730109, 41804013), the Natural Science Foundation of Jiangsu Province (Grant No. BK20200646, BK20200664), the Fundamental Re-search Funds for the Central Universi-ties (Grant No. 2020QN31, 2020QN30), the Project funded by China Postdoc-toral Science Foundation (Grant No. 2020M671645), the Open Fund of Key Laboratory for Synergistic Prevention of Water and Soil Environmental Pollution (Grant No. KLSPWSEP-A06), A Project Funded by the Priority Academic Pro-gram Development of Jiangsu Higher Education Institutions (Surveying and Mapping).Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] image Formosat/COSMIC
[Termes IGN] modèle ionosphérique
[Termes IGN] Perceptron multicouche
[Termes IGN] réseau neuronal artificiel
[Termes IGN] teneur totale en électrons
[Termes IGN] variation saisonnièreRésumé : (auteur) The ionosphere plays an important role in satellite navigation, radio communication, and space weather prediction. However, it is still a challenging mission to develop a model with high predictability that captures the horizontal-vertical features of ionospheric electrodynamics. In this study, multiple observations during 2005–2019 from space-borne global navigation satellite system (GNSS) radio occultation (RO) systems (COSMIC and FY-3C) and the Digisonde Global Ionosphere Radio Observatory are utilized to develop a completely global ionospheric three-dimensional electron density model based on an artificial neural network, namely ANN-TDD. The correlation coefficients of the predicted profiles all exceed 0.96 for the training, validation and test datasets, and the minimum root-mean-square error of the predicted residuals is 7.8 × 104 el/cm3. Under quiet space weather, the predicted accuracy of the ANN-TDD is 30%–60% higher than the IRI-2016 at the Millstone Hill and Jicamarca incoherent scatter radars. However, the ANN-TDD is less capable of predicting ionospheric dynamic evolution under severe geomagnetic storms compared to the IRI-2016 with the STORM option activated. Additionally, the ANN-TDD successfully reproduces the large-scale horizontal-vertical ionospheric electrodynamic features, including seasonal variation and hemispheric asymmetries. These features agree well with the structure revealed by the RO profiles derived from the FORMOSAT/COSMIC-2 mission. Furthermore, the ANN-TDD successfully captures the prominent regional ionospheric patterns, including the equatorial ionization anomaly, Weddell Sea anomaly and mid-latitude summer nighttime anomaly. The new model is expected to play an important role in the application of GNSS navigation and in the explanation of the physical mechanisms involved. Numéro de notice : A2021-504 Affiliation des auteurs : ENSG+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1029/2020SW002605 Date de publication en ligne : 10/03/2021 En ligne : https://doi.org/10.1029/2020SW002605 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99369
in Space weather > vol 19 n° 3 (March 2021) . - n° e2020SW002605[article]Feature detection and description for image matching: from hand-crafted design to deep learning / Lin Chen in Geo-spatial Information Science, vol 24 n° 1 (March 2021)
[article]
Titre : Feature detection and description for image matching: from hand-crafted design to deep learning Type de document : Article/Communication Auteurs : Lin Chen, Auteur ; Franz Rottensteiner, Auteur ; Christian Heipke, Auteur Année de publication : 2021 Article en page(s) : pp 58 - 74 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement automatique
[Termes IGN] appariement d'images
[Termes IGN] appariement de formes
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage profond
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image aérienne oblique
[Termes IGN] orientation d'image
[Termes IGN] SIFT (algorithme)Résumé : (Auteur) In feature based image matching, distinctive features in images are detected and represented by feature descriptors. Matching is then carried out by assessing the similarity of the descriptors of potentially conjugate points. In this paper, we first shortly discuss the general framework. Then, we review feature detection as well as the determination of affine shape and orientation of local features, before analyzing feature description in more detail. In the feature description review, the general framework of local feature description is presented first. Then, the review discusses the evolution from hand-crafted feature descriptors, e.g. SIFT (Scale Invariant Feature Transform), to machine learning and deep learning based descriptors. The machine learning models, the training loss and the respective training data of learning-based algorithms are looked at in more detail; subsequently the various advantages and challenges of the different approaches are discussed. Finally, we present and assess some current research directions before concluding the paper. Numéro de notice : A2021-297 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10095020.2020.1843376 Date de publication en ligne : 17/11/2020 En ligne : https://doi.org/10.1080/10095020.2020.1843376 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97379
in Geo-spatial Information Science > vol 24 n° 1 (March 2021) . - pp 58 - 74[article]A graph-based semi-supervised approach to classification learning in digital geographies / Pengyuan Liu in Computers, Environment and Urban Systems, vol 86 (March 2021)
[article]
Titre : A graph-based semi-supervised approach to classification learning in digital geographies Type de document : Article/Communication Auteurs : Pengyuan Liu, Auteur ; Stefano de Sabbata, Auteur Année de publication : 2021 Article en page(s) : n° 101583 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] apprentissage profond
[Termes IGN] approche participative
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification semi-dirigée
[Termes IGN] données spatiotemporelles
[Termes IGN] partage de données localisées
[Termes IGN] prise en compte du contexte
[Termes IGN] réseau social
[Termes IGN] segmentation sémantique
[Termes IGN] Time-geographyRésumé : (auteur) As the distinction between online and physical spaces rapidly degrades, social media have now become an integral component of how many people's everyday experiences are mediated. As such, increasing interest has emerged in exploring how the content shared through those online platforms comes to contribute to the collaborative creation of places in physical space at the urban scale. Exploring digital geographies of social media data using methods such as qualitative coding (i.e., content labelling) is a flexible but complex task, commonly limited to small samples due to its impracticality over large datasets. In this paper, we propose a new tool for studies in digital geographies, bridging qualitative and quantitative approaches, able to learn a set of arbitrary labels (qualitative codes) on a small, manually-created sample and apply the same labels on a larger set. We introduce a semi-supervised, deep neural network approach to classify geo-located social media posts based on their textual and image content, as well as geographical and temporal aspects. Our innovative approach is rooted in our understanding of social media posts as augmentations of the time-space configurations that places are, and it comprises a stacked multi-modal autoencoder neural network to create joint representations of text and images, and a spatio-temporal graph convolution neural network for semi-supervised classification. The results presented in this paper show that our approach performs the classification of social media content with higher accuracy than traditional machine learning models as well as two state-of-art deep learning frameworks. Numéro de notice : A2021-024 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2020.101583 Date de publication en ligne : 16/12/2020 En ligne : https://doi.org/10.1016/j.compenvurbsys.2020.101583 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96608
in Computers, Environment and Urban Systems > vol 86 (March 2021) . - n° 101583[article]Graph convolutional autoencoder model for the shape coding and cognition of buildings in maps / Xiongfeng Yan in International journal of geographical information science IJGIS, vol 35 n° 3 (March 2021)PermalinkLightweight convolutional neural network-based pedestrian detection and re-identification in multiple scenarios / Xiao Ke in Machine Vision and Applications, vol 32 n° 2 (March 2021)PermalinkMachine learning in ground motion prediction / Farid Khosravikia in Computers & geosciences, vol 148 (March 2021)PermalinkMulti-level progressive parallel attention guided salient object detection for RGB-D images / Zhengyi Liu in The Visual Computer, vol 37 n° 3 (March 2021)PermalinkPBNet: 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)PermalinkRobust 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)PermalinkSuitability assessment of urban land use in Dalian, China using PNN and GIS / Ziqian Kang 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)Permalink