IEEE Transactions on geoscience and remote sensing / IEEE Geoscience and remote sensing society (Etats-Unis) . vol 60 n° 2Paru le : 01/02/2022 |
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Ajouter le résultat dans votre panierA combination of convolutional and graph neural networks for regularized road surface extraction / Jingjing Yan in IEEE Transactions on geoscience and remote sensing, vol 60 n° 2 (February 2022)
[article]
Titre : A combination of convolutional and graph neural networks for regularized road surface extraction Type de document : Article/Communication Auteurs : Jingjing Yan, Auteur ; Shunping Ji, Auteur ; Yao Wei, Auteur Année de publication : 2022 Article en page(s) : n° 4409113 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Bavière (Allemagne)
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection de contours
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] extraction du réseau routier
[Termes IGN] image aérienne
[Termes IGN] jeu de données
[Termes IGN] optimisation (mathématiques)
[Termes IGN] régression
[Termes IGN] réseau neuronal de graphes
[Termes IGN] Wuhan (Chine)Résumé : (auteur) Road surface extraction from high-resolution remote sensing images has many engineering applications; however, extracting regularized and smooth road surface maps that reach the human delineation level is a very challenging task, and substantial and time-consuming manual work is usually unavoidable. In this article, to solve this problem, we propose a novel regularized road surface extraction framework by introducing a graph neural network (GNN) for processing the road graph that is preconstructed from the easily accessible road centerlines. The proposed framework formulates the road surface extraction problem as two-sided width inference of the road graph and consists of a convolutional neural network (CNN)-based feature extractor and a GNN model for vertex attribute adjustment. The CNN extracts the high-level abstract features of each vertex in the graph as the input of the GNN and also the road boundary features that allow us to distinguish roads from the background. The GNN propagates and aggregates the features of the vertices in the graph to achieve global optimization of the regression of the regularized widths of the vertices. At the same time, a biased centerline map can also be corrected based on the width prediction result. To the best of the authors’ knowledge, this is the first study to have introduced a GNN to regularized human-level road surface extraction. The proposed method was evaluated on four diverse datasets, and the results show that the proposed method comprehensively outperforms the recent CNN-based segmentation methods and other regularization methods in the intersection over union (IoU) and smoothness score, and a visual check shows that a majority of the prediction results of the proposed method approach the human delineation level. Numéro de notice : A2022-297 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3151688 Date de publication en ligne : 15/02/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3151688 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100355
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 2 (February 2022) . - n° 4409113[article]A batch algorithm for GNSS carrier phase cycle slip correction / Brian Breitsch in IEEE Transactions on geoscience and remote sensing, vol 60 n° 2 (February 2022)
[article]
Titre : A batch algorithm for GNSS carrier phase cycle slip correction Type de document : Article/Communication Auteurs : Brian Breitsch, Auteur ; Y. Jade Morton, Auteur Année de publication : 2022 Article en page(s) : n° 5702224 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] glissement de cycle
[Termes IGN] mesurage de phase
[Termes IGN] phase GNSS
[Termes IGN] propagation du signal
[Termes IGN] rapport signal sur bruit
[Termes IGN] scintillation
[Termes IGN] signal GNSSRésumé : (auteur) Signal-phase measurements from global navigation satellite systems (GNSSs) have become an important tool for various remote sensing applications, including measuring ionosphere plasma content, atmospheric radio occultation, and water and ice reflectometry. In these types of scenarios, GNSS signals often experience harsh propagation conditions, such as low signal-to-noise ratios, multipath, and semicoherent scattering. These conditions, in turn, lead to the frequent occurrence of cycle slips, which manifests as persistent discrete changes in the bias of the carrier phase measurement. In order to effectively use the precise GNSS phase measurements under such conditions, we argue that a window of high-rate measurements must be used. In addition, we suggest that enforcing sparsity in the occurrence of detected cycle slips can aid in detection. We, therefore, develop a batch cycle-slip detection and estimation method that is effective and computationally tractable under harsh signal conditions. This work focuses in particular on strong ionosphere scintillation, which is among the most difficult scenarios for estimating cycle slips. We demonstrate the effectiveness of our method on both simulated and real GNSS scintillation datasets, showing around a 90% reduction of slips. Numéro de notice : A2022-292 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3151416 Date de publication en ligne : 14/02/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3151416 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100360
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 2 (February 2022) . - n° 5702224[article]