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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]Partial linear NMF-based unmixing methods for detection and area estimation of photovoltaic panels in urban hyperspectral remote sensing data / Moussa Sofiane Karoui in Remote sensing, vol 11 n° 18 (September 2019)
[article]
Titre : Partial linear NMF-based unmixing methods for detection and area estimation of photovoltaic panels in urban hyperspectral remote sensing data Type de document : Article/Communication Auteurs : Moussa Sofiane Karoui, Auteur ; Fatima Zohra Benhalouche, Auteur ; Yannick Deville, Auteur ; Khelifa Djerriri, Auteur ; Xavier Briottet , Auteur ; Thomas Houet, Auteur ; Arnaud Le Bris , Auteur ; Christiane Weber, Auteur Année de publication : 2019 Projets : HYEP / Weber, Christiane Article en page(s) : n° 2164 Note générale : bibliographie
This paper constitutes a substantial extension of: https://doi.org/10.1109/IGARSS.2018.8518204Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] détection d'objet
[Termes IGN] factorisation de matrice non-négative
[Termes IGN] image hyperspectrale
[Termes IGN] panneau photovoltaïque
[Termes IGN] zone urbaineRésumé : (auteur) High-spectral-resolution hyperspectral data are acquired by sensors that gather images from hundreds of narrow and contiguous bands of the electromagnetic spectrum. These data offer unique opportunities for characterization and precise land surface recognition in urban areas. So far, few studies have been conducted with these data to automatically detect and estimate areas of photovoltaic panels, which currently constitute an important part of renewable energy systems in urban areas of developed countries. In this paper, two hyperspectral-unmixing-based methods are proposed to detect and to estimate surfaces of photovoltaic panels. These approaches, related to linear spectral unmixing (LSU) techniques, are based on new nonnegative matrix factorization (NMF) algorithms that exploit known panel spectra, which makes them partial NMF methods. The first approach, called Grd-Part-NMF, is a gradient-based method, whereas the second one, called Multi-Part-NMF, uses multiplicative update rules. To evaluate the performance of these approaches, experiments are conducted on realistic synthetic and real airborne hyperspectral data acquired over an urban region. For the synthetic data, obtained results show that the proposed methods yield much better overall performance than NMF-unmixing-based methods from the literature. For the real data, the obtained detection and area estimation results are first confirmed by using very high-spatial-resolution ortho-images of the same regions. These results are also compared with those obtained by standard NMF-unmixing-based methods and by a one-class-classification-based approach. This comparison shows that the proposed approaches are superior to those considered from the literature. Numéro de notice : A2019-430 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs11182164 Date de publication en ligne : 17/09/2019 En ligne : https://doi.org/10.3390/rs11182164 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93739
in Remote sensing > vol 11 n° 18 (September 2019) . - n° 2164[article]Detecting and mapping traffic signs from Google Street View images using deep learning and GIS / Andrew Campbell in Computers, Environment and Urban Systems, vol 77 (september 2019)
[article]
Titre : Detecting and mapping traffic signs from Google Street View images using deep learning and GIS Type de document : Article/Communication Auteurs : Andrew Campbell, Auteur ; Alan Both, Auteur ; Qian (Chayn) Sun, Auteur ; Qian (Chayn) Sun, Auteur Année de publication : 2019 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] apprentissage profond
[Termes IGN] base de données routières
[Termes IGN] détection d'objet
[Termes IGN] feu de circulation
[Termes IGN] gestion de trafic
[Termes IGN] image Streetview
[Termes IGN] réseau routier
[Termes IGN] signalisation routière
[Termes IGN] système d'information géographique
[Termes IGN] trafic routier
[Termes IGN] vision par ordinateurRésumé : (auteur) Street traffic sign infrastructure remains an extremely difficult asset for local government to manage due to its diverse physical structure and geographical distribution. A spatial registrar of traffic infrastructure is currently a required component of local government councils' mandatory road management plans. Recent advancements of object detection technology in machine learning have presented an automated approach for the detection and classification of street signage captured by Google's Street View (GSV) imagery. This paper explores the possibility of using deep learning to produce an autonomous system for detecting traffic signs on GSV images to assist in traffic assets monitoring and maintenance. By leveraging Google's Street View API, this research offers an economic approach of building purposeful street sign computer vision datasets. A custom object detection model was trained to detect and classify Stop and Give Way signs from images captured at intersection approaches. Considering the output detected bounding box coordinates, photogrammetry approach was applied to calculate the approximate location of each detected sign in two-dimensional geographical space. The newly located and classified street signs can be combined with relevant spatial data for implementation into an asset management system. By combining GIS and the GSV API, the process is completely scalable to any level of street sign classification scope. The experiments conducted on the road network of study area recorded a detection accuracy of 95.63% and classification accuracy of 97.82%. Our proposed automated approach to the detection and localisation of street sign infrastructure has displayed a promising potential for its use by local government authorities. Our workflow can be used to detect other traffic signs and applied to other road sections and other cities. Of primary importance, this approach takes an entirely free and open-source approach throughout. The continuation of Google's Street View program will account for the spatiotemporal representation of street sign infrastructure for the ongoing maintenance and renewal programs of this valuable asset. Numéro de notice : A2019-412 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.compenvurbsys.2019.101350 Date de publication en ligne : 07/06/2019 En ligne : https://doi.org/10.1016/j.compenvurbsys.2019.101350 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93536
in Computers, Environment and Urban Systems > vol 77 (september 2019)[article]Development and evaluation of a deep learning model for real-time ground vehicle semantic segmentation from UAV-based thermal infrared imagery / Mehdi Khoshboresh Masouleh in ISPRS Journal of photogrammetry and remote sensing, vol 155 (September 2019)
[article]
Titre : Development and evaluation of a deep learning model for real-time ground vehicle semantic segmentation from UAV-based thermal infrared imagery Type de document : Article/Communication Auteurs : Mehdi Khoshboresh Masouleh, Auteur ; Reza Shah-Hosseini, Auteur Année de publication : 2019 Article en page(s) : pp 172 - 186 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage profond
[Termes IGN] détection d'objet
[Termes IGN] image captée par drone
[Termes IGN] image RVB
[Termes IGN] image thermique
[Termes IGN] segmentation d'image
[Termes IGN] segmentation sémantique
[Termes IGN] véhicule automobileRésumé : (Auteur) Real-time unmanned aerial vehicles (UAVs)-based thermal infrared images processing, due to high spatial resolution and knowledge of the various infrared radiant energy level distribution of solid bodies, has important applications such as monitoring and control of the various phenomena in different natural situations. One of these applications is monitoring the ground vehicles in cities by using detection or semantic segmentation of them in the thermal images. In this research, our purpose is to improve the performance of deep learning combined model by using Gaussian-Bernoulli Restricted Boltzmann Machine (GB-RBM) specifications for the segmentation of the ground vehicles from UAV-based thermal infrared imagery. The proposed model is studied in three steps. First, designing the proposed model by using an encoder-decoder structure and addition of extracted features from convolutional layers and restricted Boltzmann machine in the network. Second, the implementation of the research goals on four sets of UAV-based thermal infrared imagery named NPU_CS_UAV_IR_DATA that was collected from some streets of China by using FLIR TAU2 thermal infrared sensor in 2017. Finally, analyzing the performance of the proposed model by using five state-of-the-art models in semantic segmentation. The results evaluated the performance of the proposed model as a robust model with the average precision and average processing time of approximately 0.97, and 19.73 s for all datasets, respectively. Numéro de notice : A2019-315 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.isprsjprs.2019.07.009 Date de publication en ligne : 25/07/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.07.009 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93341
in ISPRS Journal of photogrammetry and remote sensing > vol 155 (September 2019) . - pp 172 - 186[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2019091 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019093 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019092 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Exploitation of deep learning in the automatic detection of cracks on paved roads / Won Mo Jung in Geomatica, vol 73 n° 2 (June 2019)
[article]
Titre : Exploitation of deep learning in the automatic detection of cracks on paved roads Type de document : Article/Communication Auteurs : Won Mo Jung, Auteur ; Faizaan Naveed, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 29 - 44 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] autoroute
[Termes IGN] chaussée
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'objet
[Termes IGN] Ontario (Canada)Mots-clés libres : fissure Résumé : (auteur) With the advance of deep learning networks, their applications in the assessment of pavement conditions are gaining more attention. A convolutional neural network (CNN) is the most commonly used network in image classification. In terms of pavement assessment, most existing CNNs are designed to only distinguish between cracks and non-cracks. Few networks classify cracks in different levels of severity. Information on the severity of pavement cracks is critical for pavement repair services. In this study, the state-of-the-art CNN used in the detection of pavement cracks was improved to localize the cracks and identify their distress levels based on three categories (low, medium, and high). In addition, a fully convolutional network (FCN) was, for the first time, utilized in the detection of pavement cracks. These designed architectures were validated using the data acquired on four highways in Ontario, Canada, and compared with the ground truth that was provided by the Ministry of Transportation of Ontario (MTO). The results showed that with the improved CNN, the prediction precision on a series of test image patches were 72.9%, 73.9%, and 73.1% for cracks with the severity levels of low, medium, and high, respectively. The precision for the FCN was tested on whole pavement images, resulting in 62.8%, 63.3%, and 66.4%, respectively, for cracks with the severity levels of low, medium, and high. It is worth mentioning that the ground truth contained some uncertainties, which partially contributed to the relatively low precision. Numéro de notice : A2019-657 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1139/geomat-2019-0008 En ligne : https://doi.org/10.1139/geomat-2019-0008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98324
in Geomatica > vol 73 n° 2 (June 2019) . - pp 29 - 44[article]Patch-based detection of dynamic objects in CrowdCam images / Gagan Kanojia in The Visual Computer, vol 35 n° 4 (April 2019)PermalinkVehicle detection in aerial images / Michael Ying Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 4 (avril 2019)PermalinkAn image-pyramid-based raster-to-vector conversion (IPBRTVC) framework for consecutive-scale cartography and synchronized generalization of classic objects / Chang Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 3 (March 2019)PermalinkSemantic understanding of scenes through the ADE20K dataset / Bolei Zhou in International journal of computer vision, vol 127 n° 3 (March 2019)PermalinkDétection de fenêtres dans un nuage de points de façade et positionnement semi-automatique dans un logiciel BIM / Julie Thierry (2019)PermalinkDétection et localisation d'objets 3D par apprentissage profond en topologie capteur / Pierre Biasutti (2019)PermalinkPermalinkRobust 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)PermalinkMulti-scale object detection in remote sensing imagery with convolutional neural networks / Zhipeng Deng in ISPRS Journal of photogrammetry and remote sensing, vol 145 - part A (November 2018)PermalinkAutomated extraction of 3D vector topographic feature line from terrain point cloud / Wei Zhou in Geocarto international, vol 33 n° 10 (October 2018)Permalink