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Improving traffic sign recognition results in urban areas by overcoming the impact of scale and rotation / R. Yazdan in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 171 (January 2021) . - pp 18 - 35 Titre : Improving traffic sign recognition results in urban areas by overcoming the impact of scale and rotation Type de document : Article/Communication Auteurs : R. Yazdan, Auteur ; M. Varshosaz, Auteur Année de publication : 2021 Article en page(s) : pp 18 - 35 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] base de données d'images [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] corrélation à l'aide de traits caractéristiques [Termes descripteurs IGN] corrélation croisée normalisée [Termes descripteurs IGN] couple stéréoscopique [Termes descripteurs IGN] détection automatique [Termes descripteurs IGN] modèle stéréoscopique [Termes descripteurs IGN] reconnaissance d'objets [Termes descripteurs IGN] segmentation d'image [Termes descripteurs IGN] SIFT (algorithme) [Termes descripteurs IGN] signalisation routière [Termes descripteurs IGN] SURF (algorithme) [Termes descripteurs IGN] Téhéran [Termes descripteurs IGN] transformation de Hough [Termes descripteurs IGN] zone urbaine Résumé : (auteur) Automatic detection and recognition of traffic signs have many applications. However, some problems can affect the accuracy of the existing algorithms, such as changes in environmental light conditions, shadows, the presence of objects of the same colour, significant changes in scale and rotation, as well as obstacles in front of the traffic signs. To overcome these difficulties, a reference image database is usually used that includes different modes of appearing the traffic signs in the images. In order to overcome the effects of scale and rotation, in this paper a new method is presented in which only one reference image is needed for each sign to recognise the traffic sign in an image. In the proposed method, imaging is done in stereo. Using the captured image pair, a virtual image is generated which is then used to recognise the sign. As a result, the recognition is carried out with a minimum number of reference images. Experiments show that the proposed algorithm significantly improves recognition results. The traffic signs are recognised with 93.1% accuracy that enjoys a 4.9% improvement over traditional methods. Numéro de notice : A2021-010 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.10.003 date de publication en ligne : 06/11/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.10.003 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96304 [article]

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Privacy-aware visualization of volunteered geographic information (VGI) to analyze spatial activity: A benchmark implementation / Alexander Dunkel in ISPRS International journal of geo-information, vol 9 n° 10 (October 2020)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 10 (October 2020) . - 20 p. Titre : Privacy-aware visualization of volunteered geographic information (VGI) to analyze spatial activity: A benchmark implementation Type de document : Article/Communication Auteurs : Alexander Dunkel, Auteur ; Marc Löchner, Auteur ; Dirk Burghardt, Auteur Année de publication : 2020 Article en page(s) : 20 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse spatiale [Termes descripteurs IGN] base de données d'images [Termes descripteurs IGN] chaîne de traitement [Termes descripteurs IGN] confidentialité [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] protection de la vie privée [Termes descripteurs IGN] réseau social [Termes descripteurs IGN] ressources naturelles [Termes descripteurs IGN] réutilisation des données [Termes descripteurs IGN] visualisation de données Résumé : (auteur) Through volunteering data, people can help assess information on various aspects of their surrounding environment. Particularly in natural resource management, Volunteered Geographic Information (VGI) is increasingly recognized as a significant resource, for example, supporting visitation pattern analysis to evaluate collective values and improve natural well-being. In recent years, however, user privacy has become an increasingly important consideration. Potential conflicts often emerge from the fact that VGI can be re-used in contexts not originally considered by volunteers. Addressing these privacy conflicts is particularly problematic in natural resource management, where visualizations are often explorative, with multifaceted and sometimes initially unknown sets of analysis outcomes. In this paper, we present an integrated and component-based approach to privacy-aware visualization of VGI, specifically suited for application to natural resource management. As a key component, HyperLogLog (HLL)—a data abstraction format—is used to allow estimation of results, instead of more accurate measurements. While HLL alone cannot preserve privacy, it can be combined with existing approaches to improve privacy while, at the same time, maintaining some flexibility of analysis. Together, these components make it possible to gradually reduce privacy risks for volunteers at various steps of the analytical process. A specific use case demonstration is provided, based on a global, publicly-available dataset that contains 100 million photos shared by 581,099 users under Creative Commons licenses. Both the data processing pipeline and resulting dataset are made available, allowing transparent benchmarking of the privacy–utility tradeoffs. Numéro de notice : A2020-664 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9100607 date de publication en ligne : 20/10/2020 En ligne : https://doi.org/10.3390/ijgi9100607 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96141 [article]

A worldwide 3D GCP database inherited from 20 years of massive multi-satellite observations / Laure Chandelier in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-2 (August 2020)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > V-2 (August 2020) . - pp 15 - 23 Titre : A worldwide 3D GCP database inherited from 20 years of massive multi-satellite observations Type de document : Article/Communication Auteurs : Laure Chandelier , Auteur ; Laurent Coeurdevey, Auteur ; Sébastien Bosch, Auteur ; Pascal Favé, Auteur ; Roland Gachet, Auteur ; Alain Orsoni, Auteur ; Thomas Tilak , Auteur ; Alexis Barot, Auteur Année de publication : 2020 Projets : 1-Pas de projet / Conférence : ISPRS 2020, Commission 2, virtual Congress, Imaging today foreseeing tomorrow 31/08/2020 02/09/2020 Nice (en ligne) France Annals Commission 2 Article en page(s) : pp 15 - 23 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] base de données d'images [Termes descripteurs IGN] compensation par bloc [Termes descripteurs IGN] données localisées de référence [Termes descripteurs IGN] formatage [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] image multi sources [Termes descripteurs IGN] image satellite [Termes descripteurs IGN] image SPOT 6 [Termes descripteurs IGN] image SPOT 7 [Termes descripteurs IGN] image SPOT-HRS [Termes descripteurs IGN] informatique en nuage [Termes descripteurs IGN] Institut national de l'information géographique et forestière (France) [Termes descripteurs IGN] point d'appui [Termes descripteurs IGN] spatiotriangulation Résumé : (auteur) High location accuracy is a major requirement for satellite image users. Target performance is usually achieved thanks to either specific on-board satellite equipment or an auxiliary registration reference dataset. Both methods may be expensive and with certain limitations in terms of performance. The Institut national de l’information géographique et forestière (IGN) and Airbus Defence and Space (ADS) have worked together for almost 20 years, to build reference data for improving image location using multi-satellite observations. The first geometric foundation created has mainly used SPOT 5 High Resolution Stereoscopic (HRS) imagery, ancillary Ground Control Points (GCP) and Very High Resolution (VHR) imagery, providing a homogenous location accuracy of 10m CE90 almost all over the world in 2010. Space Reference Points (SRP) is a new worldwide 3D GCP database, built from a plethoric SPOT 6/7 multi-view archive, largely automatically processed, with cloud-based technologies. SRP aims at providing a systematic and reliable solution for image location (Unmanned Aerial Vehicle, VHR satellite imagery, High Altitudes Pseudo-Satellite…) and similar topics thanks to a high-density point distribution with a 3m CE90 accuracy. This paper describes the principle of SRP generation and presents the first validation results. A SPOT 6/7 smart image selection is performed to keep only relevant images for SRP purpose. The location of these SPOT 6/7 images is refined thanks to a spatiotriangulation on the worldwide geometric foundation, itself improved where needed. Points making up the future SRP database are afterward extracted thanks to classical feature detection algorithms and with respect to the expected density. Different filtering methods are applied to keep the best candidates. The last step of the processing chain is the formatting of the data to the delivery format, including metadata. An example of validation of SRP concept and specification on two tests sites (Spain and China) is then given. As a conclusion, the on-going production is shortly presented. Numéro de notice : A2020-474 Affiliation des auteurs : IGN+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-2-2020-15-2020 date de publication en ligne : 03/08/2020 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2020-15-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95613 [article]

Indoor positioning using PnP problem on mobile phone images / Hana Kubickova in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 6 (June 2020) . - 19 p. Titre : Indoor positioning using PnP problem on mobile phone images Type de document : Article/Communication Auteurs : Hana Kubickova, Auteur ; Karel Jedlička, Auteur ; Radek Fiala, Auteur ; Daniel Beran, Auteur Année de publication : 2020 Article en page(s) : 19 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] appariement d'images [Termes descripteurs IGN] base de données d'images [Termes descripteurs IGN] décomposition d'image [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] géométrie épipolaire [Termes descripteurs IGN] GNSS-INS [Termes descripteurs IGN] point d'appui [Termes descripteurs IGN] positionnement en intérieur [Termes descripteurs IGN] recherche d'image basée sur le contenu [Termes descripteurs IGN] SIFT (algorithme) [Termes descripteurs IGN] téléphone intelligent [Termes descripteurs IGN] vision par ordinateur Résumé : (auteur) As people grow accustomed to effortless outdoor navigation, there is a rising demand for similar possibilities indoors as well. Unfortunately, indoor localization, being one of the requirements for navigation, continues to be a problem without a clear solution. In this article, we are proposing a method for an indoor positioning system using a single image. This is made possible using a small preprocessed database of images with known control points as the only preprocessing needed. Using feature detection with the SIFT (Scale Invariant Feature Transform) algorithm, we can look through the database and find an image that is the most similar to the image taken by a user. Such a pair of images is then used to find coordinates of a database of images using the PnP problem. Furthermore, projection and essential matrices are determined to calculate the user image localization—determining the position of the user in the indoor environment. The benefits of this approach lie in the single image being the only input from a user and the lack of requirements for new onsite infrastructure. Thus, our approach enables a more straightforward realization for building management. Numéro de notice : A2020-309 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9060368 date de publication en ligne : 02/06/2020 En ligne : https://doi.org/10.3390/ijgi9060368 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95156 [article]

Automated terrain feature identification from remote sensing imagery: a deep learning approach / Wenwen Li in International journal of geographical information science IJGIS, vol 34 n° 4 (April 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 4 (April 2020) . - pp 637 - 660 Titre : Automated terrain feature identification from remote sensing imagery: a deep learning approach Type de document : Article/Communication Auteurs : Wenwen Li, Auteur ; Chia-Yu Hsu, Auteur Année de publication : 2020 Article en page(s) : pp 637 - 660 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] analyse d'image orientée objet [Termes descripteurs IGN] analyse du paysage [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] base de données d'images [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] compréhension de l'image [Termes descripteurs IGN] détection automatique [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] intelligence artificielle Résumé : (auteur) Terrain feature detection is a fundamental task in terrain analysis and landscape scene interpretation. Discovering where a specific feature (i.e. sand dune, crater, etc.) is located and how it evolves over time is essential for understanding landform processes and their impacts on the environment, ecosystem, and human population. Traditional induction-based approaches are challenged by their inefficiency for generalizing diverse and complex terrain features as well as their performance for scalable processing of the massive geospatial data available. This paper presents a new deep learning (DL) approach to support automatic detection of terrain features from remotely sensed images. The novelty of this work lies in: (1) a terrain feature database containing 12,000 remotely sensed images (1,000 original images and 11,000 derived images from data augmentation) that supports data-driven model training and new discovery; (2) a DL-based object detection network empowered by ensemble learning and deep and deeper convolutional neural networks to achieve high-accuracy object detection; and (3) fine-tuning the model’s characteristics and behaviors to identify the best combination of hyperparameters and other network factors. The introduction of DL into geospatial applications is expected to contribute significantly to intelligent terrain analysis, landscape scene interpretation, and the maturation of spatial data science. Numéro de notice : A2020-108 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1542697 date de publication en ligne : 07/11/2018 En ligne : https://doi.org/10.1080/13658816.2018.1542697 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94708 [article]

Challenging deep image descriptors for retrieval in heterogeneous iconographic collections / Dimitri Gominski (2019)  

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Google Earth Engine applications / Lalit Kumar (2019)  

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Image based rendering of large historical image collections / Evelyn Paiz-Reyes (2019)    

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Machine learning for image segmentation / Kaiwen Chang (2019)  

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Raffinement de la localisation d’images provenant de sites participatifs pour la mise à jour de SIG urbain / Bernard Semaan (2018)  

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Réseaux de neurones convolutionnels profonds pour la détection de petits véhicules en imagerie aérienne / Jean Ogier du Terrail (2018)  

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Combination of image descriptors for the exploration of cultural photographic collections / Neelanjan Bhowmik in Journal of Electronic Imaging, vol 26 n° 1 (January - February 2017)  

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Recherche multi-descripteurs dans les fonds photographiques numérisés / Neelanjan Bhowmik (2017)  

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Spectral band selection for urban material classification using hyperspectral libraries / Arnaud Le Bris in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, III-7 (July 2016)  

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Indoor navigation of mobile robots based on visual memory and image-based visual servoing / Suman Raj Bista (2016)  

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