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Termes IGN > mathématiques > statistique mathématique > analyse de données > segmentation > segmentation sémantique
segmentation sémantiqueSynonyme(s)étiquetage sémantique étiquetage de données |
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Unsupervised semantic and instance segmentation of forest point clouds / Di Wang in ISPRS Journal of photogrammetry and remote sensing, vol 165 (July 2020)
[article]
Titre : Unsupervised semantic and instance segmentation of forest point clouds Type de document : Article/Communication Auteurs : Di Wang, Auteur Année de publication : 2020 Article en page(s) : pp 86 - 97 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse de groupement
[Termes IGN] classification non dirigée
[Termes IGN] données lidar
[Termes IGN] hauteur des arbres
[Termes IGN] houppier
[Termes IGN] indice foliaire
[Termes IGN] interprétation automatique
[Termes IGN] segmentation sémantique
[Termes IGN] semis de points
[Termes IGN] télémètre laser terrestreRésumé : (auteur) Terrestrial Laser Scanning (TLS) has been increasingly used in forestry applications including forest inventory and plant ecology. Tree biophysical properties such as leaf area distributions and wood volumes can be accurately estimated from TLS point clouds. In these applications, a prerequisite is to properly understand the information content of large scale point clouds (i.e., semantic labelling of point clouds), so that tree-scale attributes can be retrieved. Currently, this requirement is undergoing laborious and time consuming manual works. In this work, we jointly address the problems of semantic and instance segmentation of forest point clouds. Specifically, we propose an unsupervised pipeline based on a structure called superpoint graph, to simultaneously perform two tasks: single tree isolation and leaf-wood classification. The proposed method is free from restricted assumptions of forest types. Validation using simulated data resulted in a mean Intersection over Union (mIoU) of 0.81 for single tree isolation, and an overall accuracy of 87.7% for leaf-wood classification. The single tree isolation led to a relative root mean square error (RMSE%) of 2.9% and 19.8% for tree height and crown diameter estimations, respectively. Comparisons with existing methods on other benchmark datasets showed state-of-the-art results of our method on both single tree isolation and leaf-wood classification tasks. We provide the entire framework as an open-source tool with an end-user interface. This study closes the gap for using TLS point clouds to quantify tree-scale properties in large areas, where automatic interpretation of the information content of TLS point clouds remains a crucial challenge. Numéro de notice : A2020-347 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.04.020 Date de publication en ligne : 28/05/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.04.020 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95228
in ISPRS Journal of photogrammetry and remote sensing > vol 165 (July 2020) . - pp 86 - 97[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2020071 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020073 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020072 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Counting of grapevine berries in images via semantic segmentation using convolutional neural networks / Laura Zabawa in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)
[article]
Titre : Counting of grapevine berries in images via semantic segmentation using convolutional neural networks Type de document : Article/Communication Auteurs : Laura Zabawa, Auteur ; Anna Kicherer, Auteur ; Lasse Klingbeil, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 73 - 83 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] comptage
[Termes IGN] échantillon
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] extraction semi-automatique
[Termes IGN] régression
[Termes IGN] rendement agricole
[Termes IGN] segmentation sémantique
[Termes IGN] traitement d'image
[Termes IGN] viticultureRésumé : (auteur) The extraction of phenotypic traits is often very time and labour intensive. Especially the investigation in viticulture is restricted to an on-site analysis due to the perennial nature of grapevine. Traditionally skilled experts examine small samples and extrapolate the results to a whole plot. Thereby different grapevine varieties and training systems, e.g. vertical shoot positioning (VSP) and semi minimal pruned hedges (SMPH) pose different challenges.
In this paper we present an objective framework based on automatic image analysis which works on two different training systems. The images are collected semi automatic by a camera system which is installed in a modified grape harvester. The system produces overlapping images from the sides of the plants. Our framework uses a convolutional neural network to detect single berries in images by performing a semantic segmentation. Each berry is then counted with a connected component algorithm. We compare our results with the Mask-RCNN, a state-of-the-art network for instance segmentation and with a regression approach for counting. The experiments presented in this paper show that we are able to detect green berries in images despite of different training systems. We achieve an accuracy for the berry detection of 94.0% in the VSP and 85.6% in the SMPH.Numéro de notice : A2020-252 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.04.002 Date de publication en ligne : 22/04/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.04.002 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94996
in ISPRS Journal of photogrammetry and remote sensing > vol 164 (June 2020) . - pp 73 - 83[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2020061 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020063 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020062 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt A hybrid deep learning–based model for automatic car extraction from high-resolution airborne imagery / Mehdi Khoshboresh Masouleh in Applied geomatics, vol 12 n° 2 (June 2020)
[article]
Titre : A hybrid deep learning–based model for automatic car extraction from high-resolution airborne imagery Type de document : Article/Communication Auteurs : Mehdi Khoshboresh Masouleh, Auteur ; Reza Shah-Hosseini, Auteur Année de publication : 2020 Article en page(s) : pp 107 - 119 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] extraction automatique
[Termes IGN] gestion de trafic
[Termes IGN] image à haute résolution
[Termes IGN] image aérienne
[Termes IGN] modèle orienté objet
[Termes IGN] orthophotographie
[Termes IGN] segmentation sémantique
[Termes IGN] trafic routier
[Termes IGN] véhicule automobileRésumé : (auteur) Automatic car extraction (ACE) from high-resolution airborne imagery (i.e., true-orthophoto) has been a hot research topic in the field of photogrammetry and machine learning. ACE from high-resolution airborne imagery is the most suitable method for control and monitoring practices in large cities such as traffic management. The use of deep learning–based feature extraction methods, such as convolutional neural networks, have been providing state-of-the-art performance in the last few years, particularly, these techniques have been successfully applied to automatic object extraction from images. In this paper, we proposed a novel hybrid method to take advantage of the semantic segmentation of high-resolution airborne imagery to ACE that is realized based on the combination of deep convolutional neural networks and restricted Boltzmann machine (RBM). This hybrid method is called RBMDeepNet. We trained and tested our model on the ISPRS Potsdam and Vaihingen benchmark datasets (non-big data) which is more challenging for ACE. Here, Potsdam data is a true-color dataset, and Vaihingen data is a false-color dataset. The results obtained in the present study showed that the proposed method for ACE from high-resolution airborne imagery achieves a 7% improvement in accuracy with about 10% improvement in processing time compared to similar methods. Numéro de notice : A2020-558 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s12518-019-00285-4 Date de publication en ligne : 06/08/2019 En ligne : https://doi.org/10.1007/s12518-019-00285-4 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95868
in Applied geomatics > vol 12 n° 2 (June 2020) . - pp 107 - 119[article]Comparing the roles of landmark visual salience and semantic salience in visual guidance during indoor wayfinding / Weihua Dong in Cartography and Geographic Information Science, vol 47 n° 3 (May 2020)
[article]
Titre : Comparing the roles of landmark visual salience and semantic salience in visual guidance during indoor wayfinding Type de document : Article/Communication Auteurs : Weihua Dong, Auteur ; Tong Qin, Auteur ; Hua Liao, Auteur Année de publication : 2020 Article en page(s) : pp 229 - 243 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse visuelle
[Termes IGN] interprétation (psychologie)
[Termes IGN] oculométrie
[Termes IGN] point de repère
[Termes IGN] questionnaire
[Termes IGN] saillance
[Termes IGN] scène intérieure
[Termes IGN] segmentation sémantique
[Termes IGN] test statistique
[Termes IGN] vision
[Termes IGN] vision par ordinateur
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) Landmark visual salience (characterized by features that contrast with their surroundings and visual peculiarities) and semantic salience (characterized by features with unusual or important meaning and content in the environment) are two important factors that affect an individual’s visual attention during wayfinding. However, empirical evidence regarding which factor dominates visual guidance during indoor wayfinding is rare, especially in real-world environments. In this study, we assumed that semantic salience dominates the guidance of visual attention, which means that semantic salience will correlate with participants’ fixations more significantly than visual salience. Notably, in previous studies, semantic salience was shown to guide visual attention in static images or familiar scenes in a laboratory environment. To validate this assumption, first, we collected the eye movement data of 22 participants as they found their way through a building. We then computed the landmark visual and semantic salience using computer vision models and questionnaires, respectively. Finally, we conducted correlation tests to verify our assumption. The results failed to validate our assumption and show that the role of salience in visual guidance in a real-world wayfinding process is different from the role of salience in perceiving static images or scenes in a laboratory. Visual salience dominates visual attention during indoor wayfinding, but the roles of salience in visual guidance are mixed across different landmark classes and tasks. The results provide new evidence for understanding how pedestrians visually interpret landmark information during real-world indoor wayfinding. Numéro de notice : A2020-169 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2019.1697965 Date de publication en ligne : 18/12/2019 En ligne : https://doi.org/10.1080/15230406.2019.1697965 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94841
in Cartography and Geographic Information Science > vol 47 n° 3 (May 2020) . - pp 229 - 243[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 032-2020031 RAB Revue Centre de documentation En réserve L003 Disponible How much do we learn from addresses? On the syntax, semantics and pragmatics of addressing systems / Ali Javidaneh in ISPRS International journal of geo-information, vol 9 n° 5 (May 2020)
[article]
Titre : How much do we learn from addresses? On the syntax, semantics and pragmatics of addressing systems Type de document : Article/Communication Auteurs : Ali Javidaneh, Auteur ; Farid Karimipour, Auteur ; Negar Alinaghi, Auteur Année de publication : 2020 Article en page(s) : 27 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] adresse postale
[Termes IGN] appariement d'adresses
[Termes IGN] cognition
[Termes IGN] géocodage par adresse postale
[Termes IGN] modèle orienté agent
[Termes IGN] représentation mentale spatiale
[Termes IGN] segmentation sémantique
[Termes IGN] structure syntaxiqueRésumé : (auteur) An address is a specification that refers to a unique location on Earth. While there has been a considerable amount of research on the syntactic structure of addressing systems in order to evaluate and improve their quality, aspects of semantics and pragmatics have been less explored. An address is primarily associated by humans to the elements of their spatial mental representations, but may also influence their spatial knowledge and activities through the level of detail it provides. Therefore, it is not only important how addressing components are structured, but it is also of interest to study their meaning as well as the pragmatics in relation to an interpreting agent. This article studies three forms of addresses (i.e., structured as in Austria, semi-formal as in Japan, and descriptive as in Iran) under the principles of semiotics (i.e., through levels of syntax, semantics, and pragmatics). Syntax is discussed through formal definitions of the addressing systems, while semantics and pragmatics are assessed through an agent-based model to explore how they influence spatial knowledge acquisition and growth. Numéro de notice : A2020-302 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9050317 Date de publication en ligne : 11/05/2020 En ligne : https://doi.org/10.3390/ijgi9050317 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95142
in ISPRS International journal of geo-information > vol 9 n° 5 (May 2020) . - 27 p.[article]Directionally constrained fully convolutional neural network for airborne LiDAR point cloud classification / Congcong Wen in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)PermalinkSea-land segmentation using deep learning techniques for Landsat-8 OLI imagery / Ting Yang in Marine geodesy, Vol 43 n° 2 (March 2020)PermalinkTree annotations in LiDAR data using point densities and convolutional neural networks / Ananya Gupta in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)PermalinkAnalyse, structuration et sémantisation des images aériennes [diaporama] / Valérie Gouet-Brunet (2020)PermalinkApplication of machine learning techniques for evidential 3D perception, in the context of autonomous driving / Edouard Capellier (2020)PermalinkCartographie sémantique hybride de scènes urbaines à partir de données image et Lidar / Mohamed Boussaha (2020)PermalinkConvolutional neural networks for change analysis in earth observation images with noisy labels and domain shifts / Rodrigo Caye Daudt (2020)PermalinkPermalinkDeep learning for remote sensing images with open source software / Rémi Cresson (2020)PermalinkPermalink