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Ship identification and characterization in Sentinel-1 SAR images with multi-task deep learning / Clément Dechesne in Remote sensing, Vol 11 n° 24 (December-2 2019)
[article]
Titre : Ship identification and characterization in Sentinel-1 SAR images with multi-task deep learning Type de document : Article/Communication Auteurs : Clément Dechesne , Auteur ; Sébastien Lefèvre, Auteur ; Rodolphe Vadaine, Auteur ; Guillaume Hajduch, Auteur ; Ronan Fablet, Auteur Année de publication : 2019 Projets : SESAME / Fablet, Ronan Article en page(s) : n° 2997 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'objet
[Termes IGN] détection de cible
[Termes IGN] image Sentinel-SAR
[Termes IGN] navire
[Termes IGN] objet mobileRésumé : (auteur) The monitoring and surveillance of maritime activities are critical issues in both military and civilian fields, including among others fisheries’ monitoring, maritime traffic surveillance, coastal and at-sea safety operations, and tactical situations. In operational contexts, ship detection and identification is traditionally performed by a human observer who identifies all kinds of ships from a visual analysis of remotely sensed images. Such a task is very time consuming and cannot be conducted at a very large scale, while Sentinel-1 SAR data now provide a regular and worldwide coverage. Meanwhile, with the emergence of GPUs, deep learning methods are now established as state-of-the-art solutions for computer vision, replacing human intervention in many contexts. They have been shown to be adapted for ship detection, most often with very high resolution SAR or optical imagery. In this paper, we go one step further and investigate a deep neural network for the joint classification and characterization of ships from SAR Sentinel-1 data. We benefit from the synergies between AIS (Automatic Identification System) and Sentinel-1 data to build significant training datasets. We design a multi-task neural network architecture composed of one joint convolutional network connected to three task specific networks, namely for ship detection, classification, and length estimation. The experimental assessment shows that our network provides promising results, with accurate classification and length performance (classification overall accuracy: 97.25%, mean length error: 4.65 m ± 8.55 m). Numéro de notice : A2019-632 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs11242997 Date de publication en ligne : 13/12/2019 En ligne : https://doi.org/10.3390/rs11242997 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95325
in Remote sensing > Vol 11 n° 24 (December-2 2019) . - n° 2997[article]Half a percent of labels is enough: efficient animal detection in UAV imagery using deep CNNs and active learning / Benjamin Kellenberger in IEEE Transactions on geoscience and remote sensing, vol 57 n° 12 (December 2019)
[article]
Titre : Half a percent of labels is enough: efficient animal detection in UAV imagery using deep CNNs and active learning Type de document : Article/Communication Auteurs : Benjamin Kellenberger, Auteur ; Diego Marcos, Auteur ; Sylvain Lobry, Auteur ; Devis Tuia, Auteur Année de publication : 2019 Article en page(s) : pp 9524 - 9533 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] apprentissage profond
[Termes IGN] classification orientée objet
[Termes IGN] classification par réseau neuronal
[Termes IGN] détection d'objet
[Termes IGN] données localisées
[Termes IGN] échantillonnage de données
[Termes IGN] faune locale
[Termes IGN] image captée par drone
[Termes IGN] Namibie
[Termes IGN] objet mobile
[Termes IGN] réalité de terrain
[Termes IGN] recensementRésumé : (auteur) We present an Active Learning (AL) strategy for reusing a deep Convolutional Neural Network (CNN)-based object detector on a new data set. This is of particular interest for wildlife conservation: given a set of images acquired with an Unmanned Aerial Vehicle (UAV) and manually labeled ground truth, our goal is to train an animal detector that can be reused for repeated acquisitions, e.g., in follow-up years. Domain shifts between data sets typically prevent such a direct model application. We thus propose to bridge this gap using AL and introduce a new criterion called Transfer Sampling (TS). TS uses Optimal Transport (OT) to find corresponding regions between the source and the target data sets in the space of CNN activations. The CNN scores in the source data set are used to rank the samples according to their likelihood of being animals, and this ranking is transferred to the target data set. Unlike conventional AL criteria that exploit model uncertainty, TS focuses on very confident samples, thus allowing quick retrieval of true positives in the target data set, where positives are typically extremely rare and difficult to find by visual inspection. We extend TS with a new window cropping strategy that further accelerates sample retrieval. Our experiments show that with both strategies combined, less than half a percent of oracle-provided labels are enough to find almost 80% of the animals in challenging sets of UAV images, beating all baselines by a margin. Numéro de notice : A2019-598 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2927393 Date de publication en ligne : 20/08/2019 En ligne : http://doi.org/10.1109/TGRS.2019.2927393 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94592
in IEEE Transactions on geoscience and remote sensing > vol 57 n° 12 (December 2019) . - pp 9524 - 9533[article]Scene context-driven vehicle detection in high-resolution aerial images / Chao Tao in IEEE Transactions on geoscience and remote sensing, Vol 57 n° 10 (October 2019)
[article]
Titre : Scene context-driven vehicle detection in high-resolution aerial images Type de document : Article/Communication Auteurs : Chao Tao, Auteur ; Li Mi, Auteur ; Yansheng Li, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 7339 - 7351 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification orientée objet
[Termes IGN] détection d'objet
[Termes IGN] image à haute résolution
[Termes IGN] image aérienne
[Termes IGN] objet mobile
[Termes IGN] véhicule automobileRésumé : (auteur) As the spatial resolution of remote sensing images is improving gradually, it is feasible to realize “scene-object” collaborative image interpretation. Unfortunately, this idea is not fully utilized in vehicle detection from high-resolution aerial images, and most of the existing methods may be promoted by considering the variability of vehicle spatial distribution in different image scenes and treating vehicle detection tasks scene-specific. With this motivation, a scene context-driven vehicle detection method is proposed in this paper. At first, we perform scene classification using the deep learning method and, then, detect vehicles in roads and parking lots separately through different vehicle detectors. Afterward, we further optimize the detection results using different postprocessing rules according to different scene types. Experimental results show that the proposed approach outperforms the state-of-the-art algorithms in terms of higher detection accuracy rate and lower false alarm rate. Numéro de notice : A2019-535 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2912985 Date de publication en ligne : 03/06/2019 En ligne : http://doi.org/10.1109/TGRS.2019.2912985 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94131
in IEEE Transactions on geoscience and remote sensing > Vol 57 n° 10 (October 2019) . - pp 7339 - 7351[article]SMSM: a similarity measure for trajectory stops and moves / Andre L. Lehmann in International journal of geographical information science IJGIS, vol 33 n° 9 (September 2019)
[article]
Titre : SMSM: a similarity measure for trajectory stops and moves Type de document : Article/Communication Auteurs : Andre L. Lehmann, Auteur ; Luis Otavio Alvares, Auteur ; Vania Bogorny, Auteur Année de publication : 2019 Article en page(s) : pp 1847 - 1872 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse spatio-temporelle
[Termes IGN] calcul d'itinéraire
[Termes IGN] durée de trajet
[Termes IGN] information sémantique
[Termes IGN] mesure de similitude
[Termes IGN] objet mobile
[Termes IGN] relation sémantique
[Termes IGN] taxi
[Termes IGN] trajet (mobilité)Résumé : (auteur) For many years trajectory similarity research has focused on raw trajectories, considering only space and time information. With the trajectory semantic enrichment, emerged the need for similarity measures that support space, time, and semantics. Although some trajectory similarity measures deal with all these dimensions, they consider only stops, ignoring the moves. We claim that, for some applications, the movement between stops is as important as the stops, and they must be considered in the similarity analysis. In this article, we propose SMSM, a novel similarity measure for semantic trajectories that considers both stops and moves. We evaluate SMSM with three trajectory datasets: (i) a synthetic trajectory dataset generated with the Hermoupolis semantic trajectory generator, (ii) a real trajectory dataset from the CRAWDAD project, and (iii) the Geolife dataset. The results show that SMSM overcomes state-of-the-art measures developed either for raw or semantic trajectories. Numéro de notice : A2019-391 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1605074 Date de publication en ligne : 24/06/2019 En ligne : https://doi.org/10.1080/13658816.2019.1605074 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93486
in International journal of geographical information science IJGIS > vol 33 n° 9 (September 2019) . - pp 1847 - 1872[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2019091 RAB Revue Centre de documentation En réserve L003 Disponible 079-2019092 RAB Revue Centre de documentation En réserve L003 Disponible Relative space-based GIS data model to analyze the group dynamics of moving objects / Mingxiang Feng in ISPRS Journal of photogrammetry and remote sensing, vol 153 (July 2019)
[article]
Titre : Relative space-based GIS data model to analyze the group dynamics of moving objects Type de document : Article/Communication Auteurs : Mingxiang Feng, Auteur ; Shih-Lung Shaw, Auteur ; Zhixiang Fang, Auteur ; Hao Cheng, Auteur Année de publication : 2019 Article en page(s) : pp 74 - 95 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] analyse spatio-temporelle
[Termes IGN] base de données d'objets mobiles
[Termes IGN] base de données orientée objet
[Termes IGN] modèle conceptuel de données
[Termes IGN] objet mobile
[Termes IGN] reconstruction d'itinéraire ou de trajectoire
[Termes IGN] SIG dynamique
[Termes IGN] UMLRésumé : (Auteur) The relative motion of moving objects is an essential research topic in geographical information science (GIScience), which supports the innovation of geodatabases, spatial indexing, and geospatial services. This analysis is very popular in the domains of urban governance, transportation engineering, logistics and geospatial information services for individuals or industrials. Importantly, data models of moving objects are one of the most crucial approaches to support the analysis for dynamic relative motion between moving objects, even in the age of big data and cloud computing. Traditional geographic information systems (GIS) usually organize moving objects as point objects in absolute coordinated space. The derivation of relative motions among moving objects is not efficient because of the additional geo-computation of transformation between absolute space and relative space. Therefore, current GISs require an innovative approach to directly store, analyze and interpret the relative relationships of moving objects to support their efficient analysis. This paper proposes a relative space-based GIS data model of moving objects (RSMO) to construct, operate and analyze moving objects’ relationships and introduces two algorithms (relationship querying and relative relationship dynamic pattern matching) to derive and analyze the dynamic relationships of moving objects. Three scenarios (epidemic spreading, tracker finding, and motion-trend derivation of nearby crowds) are implemented to demonstrate the feasibility of the proposed model. The experimental results indicates the execution times of the proposed model are approximately 5–50% those of the absolute GIS method for the same function of these three scenarios. It’s better computational performance of the proposed model when analyzing the relative relationships of moving objects than the absolute methods in a famous commercial GIS software based on this experimental results. The proposed approach fills the gap of traditional GIS and shows promise for relative space-based geo-computation, analysis and service. Numéro de notice : A2019-261 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.05.002 Date de publication en ligne : 15/05/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.05.002 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93074
in ISPRS Journal of photogrammetry and remote sensing > vol 153 (July 2019) . - pp 74 - 95[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2019071 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019073 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019072 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Patch-based detection of dynamic objects in CrowdCam images / Gagan Kanojia in The Visual Computer, vol 35 n° 4 (April 2019)PermalinkA conceptual framework for studying collective reactions to events in location-based social media / Alexander Dunkel in International journal of geographical information science IJGIS, Vol 33 n° 3-4 (March - April 2019)PermalinkLearning to segment moving objects / Pavel Tokmakov in International journal of computer vision, vol 127 n° 3 (March 2019)PermalinkPoint clouds for direct pedestrian pathfinding in urban environments / Jesus Balado in ISPRS Journal of photogrammetry and remote sensing, vol 148 (February 2019)PermalinkRobust vehicle detection in aerial images using bag-of-words and orientation aware scanning / Hailing Zhou in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)PermalinkA data model for moving regions of fixed shape in databases / Florian Heinz in International journal of geographical information science IJGIS, vol 32 n° 9-10 (September - October 2018)PermalinkA context-based geoprocessing framework for optimizing meetup location of multiple moving objects along road networks / Shaohua Wang in International journal of geographical information science IJGIS, vol 32 n° 7-8 (July - August 2018)PermalinkUsing interactions and dynamics for mining groups of moving objects from trajectory data / Corrado Loglisci in International journal of geographical information science IJGIS, vol 32 n° 7-8 (July - August 2018)PermalinkRange-image: Incorporating sensor topology for lidar point cloud processing / Pierre Biasutti in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 6 (juin 2018)PermalinkAttribute trajectory analysis : a framework to analyse attribute changes using trajectory analysis techniques / Long Zhang in International journal of geographical information science IJGIS, vol 32 n° 5-6 (May - June 2018)Permalink