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A graph attention network for road marking classification from mobile LiDAR point clouds / Lina Fang in International journal of applied Earth observation and geoinformation, vol 108 (April 2022)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 108 (April 2022) . - n° 102735 Titre : A graph attention network for road marking classification from mobile LiDAR point clouds Type de document : Article/Communication Auteurs : Lina Fang, Auteur ; Tongtong Sun, Auteur ; Shuang Wang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 102735 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] apprentissage profond [Termes IGN] attention (apprentissage automatique) [Termes IGN] classification par Perceptron multicouche [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] noeud [Termes IGN] réseau neuronal de graphes [Termes IGN] réseau routier [Termes IGN] semis de points [Termes IGN] signalisation routière Résumé : (auteur) The category of road marking is a crucial element in Mobile laser scanning systems’ (MLSs) applications such as intelligent traffic systems, high-definition maps, location and navigation services. Due to the complexity of road scenes, considerable and various categories, occlusion and uneven intensities in MLS point clouds, finely road marking classification is considered as the challenging work. This paper proposes a graph attention network named GAT_SCNet to simultaneously group the road markings into 11 categories from MLS point clouds. Concretely, the proposed GAT_SCNet model constructs serial computable subgraphs and fulfills a multi-head attention mechanism to encode the geometric, topological, and spatial relationships between the node and neighbors to generate the distinguishable descriptor of road marking. To assess the effectiveness and generalization of the GAT_SCNet model, we conduct extensive experiments on five test datasets of about 100 km in total captured by different MLS systems. Three accuracy evaluation metrics: average Precision, Recall, and of 11 categories on the test datasets exceed 91%, respectively. Accuracy evaluations and comparative studies show that our method has achieved a new state-of-the-art work on road marking classification, especially on similar linear road markings like stop lines, zebra crossings, and dotted lines. Numéro de notice : A2022-234 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1016/j.jag.2022.102735 Date de publication en ligne : 10/03/2022 En ligne : https://doi.org/10.1016/j.jag.2022.102735 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100124 [article]

Semi-supervised PolSAR image classification based on improved tri-training with a minimum spanning tree / Shuang Wang in IEEE Transactions on geoscience and remote sensing, Vol 58 n° 12 (December 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 58 n° 12 (December 2020) . - pp 8583 - 8597 Titre : Semi-supervised PolSAR image classification based on improved tri-training with a minimum spanning tree Type de document : Article/Communication Auteurs : Shuang Wang, Auteur ; Yanhe Guo, Auteur ; Wenqiang Hua, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 8583 - 8597 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] arbre aléatoire minimum [Termes IGN] classification semi-dirigée [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] image radar moirée [Termes IGN] polarimétrie radar [Termes IGN] segmentation sémantique [Termes IGN] voisinage (relation topologique) Résumé : (auteur) In this article, the terrain classifications of polarimetric synthetic aperture radar (PolSAR) images are studied. A novel semi-supervised method based on improved Tri-training combined with a neighborhood minimum spanning tree (NMST) is proposed. Several strategies are included in the method: 1) a high-dimensional vector of polarimetric features that are obtained from the coherency matrix and diverse target decompositions is constructed; 2) this vector is divided into three subvectors and each subvector consists of one-third of the polarimetric features, randomly selected. The three subvectors are used to separately train the three different base classifiers in the Tri-training algorithm to increase the diversity of classification; and 3) a help-training sample selection with the improved NMST that uses both the coherency matrix and the spatial information is adopted to select highly reliable unlabeled samples to increase the training sets. Thus, the proposed method can effectively take advantage of unlabeled samples to improve the classification. Experimental results show that with a small number of labeled samples, the proposed method achieves a much better performance than existing classification methods. Numéro de notice : A2020-743 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2988982 Date de publication en ligne : 14/05/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2988982 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96374 [article]