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Trajectory and image-based detection and identification of UAV / Yicheng Liu in The Visual Computer, vol 37 n° 7 (July 2021)
[article]
Titre : Trajectory and image-based detection and identification of UAV Type de document : Article/Communication Auteurs : Yicheng Liu, Auteur ; Luchuan Liao, Auteur ; Hao Wu, Auteur ; et al., Auteur Année de publication : 2021 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] Aves
[Termes IGN] caméra de surveillance PTZ
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'objet
[Termes IGN] drone
[Termes IGN] forme caractéristique
[Termes IGN] interférence
[Termes IGN] objet mobile
[Termes IGN] reconnaissance de formes
[Termes IGN] trajectoire (véhicule non spatial)Résumé : (auteur) Much more attentions have been attracted to the inspection and prevention of unmanned aerial vehicle (UAV) in the wake of increasing high frequency of security accident. Many factors like the interferences and the small fuselage of UAV pose challenges to the timely detection of the UAV. In our work, we present a system that is capable of detecting, recognizing, and tracking an UAV using single camera automatically. For our method, a single pan–tilt–zoom (PTZ) camera detects flying objects and gets their trajectories; then, the trajectory identified as a UAV guides the camera and PTZ to capture the detailed region image of the target. Therefore, the images can be classified into the UAV and interference classes (such as birds) by the convolution neural network classifier trained with our image dataset. For the target recognized as a UAV with the double verification, the radio jammer emits the interferential radio to disturb its control radio and GPS. This system could be applied in some complex environment where many birds and UAV appear simultaneously. Numéro de notice : A2021-541 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-020-01937-y Date de publication en ligne : 29/07/2020 En ligne : https://doi.org/10.1007/s00371-020-01937-y Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98020
in The Visual Computer > vol 37 n° 7 (July 2021)[article]An automatic workflow for orientation of historical images with large radiometric and geometric differences / Ferdinand Maiwald in Photogrammetric record, vol 36 n° 174 (June 2021)
[article]
Titre : An automatic workflow for orientation of historical images with large radiometric and geometric differences Type de document : Article/Communication Auteurs : Ferdinand Maiwald, Auteur ; Hans-Gerd Maas, Auteur Année de publication : 2021 Article en page(s) : pp 77 - 103 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement de formes
[Termes IGN] artefact
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image ancienne
[Termes IGN] plus proche voisin, algorithme du
[Termes IGN] réalité augmentée
[Termes IGN] réalité virtuelle
[Termes IGN] reconstruction 3D
[Termes IGN] scène urbaine
[Termes IGN] SIFT (algorithme)
[Termes IGN] structure-from-motionRésumé : (auteur) This contribution proposes a workflow for a completely automatic orientation of historical terrestrial urban images. Automatic structure from motion (SfM) software packages often fail when applied to historical image pairs due to large radiometric and geometric differences causing challenges with feature extraction and reliable matching. As an innovative initialising step, the proposed method uses the neural network D2-Net for feature extraction and Lowe’s mutual nearest neighbour matcher. The principal distance for every camera is estimated using vanishing point detection. The results were compared to three state-of-the-art SfM workflows (Agisoft Metashape, Meshroom and COLMAP) with the proposed workflow outperforming the other SfM tools. The resulting camera orientation data are planned to be imported into a web and virtual/augmented reality (VR/AR) application for the purpose of knowledge transfer in cultural heritage. Numéro de notice : A2021-471 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12363 Date de publication en ligne : 06/06/2021 En ligne : https://doi.org/10.1111/phor.12363 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97925
in Photogrammetric record > vol 36 n° 174 (June 2021) . - pp 77 - 103[article]Deep learning in denoising of micro-computed tomography images of rock samples / Mikhail Sidorenko in Computers & geosciences, vol 151 (June 2021)
[article]
Titre : Deep learning in denoising of micro-computed tomography images of rock samples Type de document : Article/Communication Auteurs : Mikhail Sidorenko, Auteur ; Denis Orlov, Auteur ; Mohammad Ebadi, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 104716 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] accentuation d'image
[Termes IGN] apprentissage profond
[Termes IGN] classification dirigée
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] filtrage du bruit
[Termes IGN] filtre passe-bande
[Termes IGN] roche
[Termes IGN] tomographieRésumé : (auteur) Nowadays, the advantages of Digital Rock Physics (DRP) are well known and widely applied in comprehensive core analysis. It is also known that the quality of the 3D pore scale model drastically influences the results of rock properties simulation, which makes the preprocessing stage of DRP very important. In this work, we consider the application of Deep Convolutional Neural Networks (CNNs) for the preprocessing of CT images, specifically for denoising, in two setups - conventional fully-supervised learning and the self-supervised learning, when the only available data is the noisy images. To train CNNs in a supervised setup, we use images processed by a combination of bilateral and bandpass filters. We trained CNNs of the same architecture with different loss functions to find out how the choice of a loss function influences the model's performance. Some of the obtained CNNs yielded the highest quality in terms of full-reference and no-reference metrics and significant histogram effect (bimodal intensity distribution). Images denoised with these models were qualitatively and quantitatively better than the reference “ground truth” images used for training. We use the Deep Image Prior algorithm to train denoising models in a self-supervised setup. The obtained models are much better than ones obtained in fully-supervised setup, but are too slow, as they are optimization-based rather than feed-forward. Such an algorithm can be used in the dataset generation for feed-forward meta-models. These results could help to develop an AI-based instrument to build high-quality 3D segmented models of rocks for DRP applications. Numéro de notice : A2021-389 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1016/j.cageo.2021.104716 Date de publication en ligne : 02/03/2021 En ligne : https://doi.org/10.1016/j.cageo.2021.104716 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97672
in Computers & geosciences > vol 151 (June 2021) . - n° 104716[article]Impact of different sampling rates on precise point positioning performance using online processing service / Serdar Erol in Geo-spatial Information Science, vol 24 n° 2 (June 2021)
[article]
Titre : Impact of different sampling rates on precise point positioning performance using online processing service Type de document : Article/Communication Auteurs : Serdar Erol, Auteur ; Reha Metin Alkan, Auteur ; I. Murat Ozulu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 302 - 312 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement
[Termes IGN] données GNSS
[Termes IGN] format RINEX
[Termes IGN] instrumentation Trimble
[Termes IGN] intervalle de confiance
[Termes IGN] phase GNSS
[Termes IGN] positionnement cinématique en temps réel
[Termes IGN] positionnement ponctuel précis
[Termes IGN] précision du positionnement
[Termes IGN] rapport signal sur bruit
[Termes IGN] réalité de terrain
[Termes IGN] retard troposphérique zénithal
[Termes IGN] taux d'échantillonnage
[Termes IGN] trajet multiple
[Termes IGN] TurquieRésumé : (auteur) In this study, the effect of different sampling rates (i.e. observation recording interval) on the Precise Point Positioning (PPP) solutions in terms of accuracy was investigated. For this purpose, a field test was carried out in Çorum province, Turkey, on 11 September 2019. Within this context, a Geodetic Point (GP) was established and precisely coordinated. A static GNSS measurement was occupied on the GP for about 4-hour time at 0.10 second (s)/10 Hz measurement intervals with the Trimble R10 geodetic grade GNSS receiver. The original observation file was converted to RINEX format and then decimated into the different data sampling rates as 0.2 s, 0.5 s, 1 s, 5 s, 10 s, 30 s, 60 s, and 120 s. All these RINEX observation files were submitted to the Canadian Spatial Reference System-Precise Point Positioning (CSRS-PPP) online processing service the day after the data collection date by choosing both static and kinematic processing options. In this way, PPP-derived static coordinates, and the kinematic coordinates of each measurement epoch were calculated. The PPP-derived coordinates obtained from each decimated sampling intervals were compared to known coordinates of the GP for northing, easting, 2D position, and height components. According to the static and kinematic processing results, high data sampling rates did not change the PPP solutions in terms of accuracy when compared to the results obtained using lower sampling rates. The results of this study imply that it was not necessary to collect GNSS data with high-rate intervals for many surveying projects requiring cm-level accuracy. Numéro de notice : A2021-558 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10095020.2020.1842811 Date de publication en ligne : 25/11/2020 En ligne : https://doi.org/10.1080/10095020.2020.1842811 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98111
in Geo-spatial Information Science > vol 24 n° 2 (June 2021) . - pp 302 - 312[article]Multi-GNSS PPP/INS tightly coupled integration with atmospheric augmentation and its application in urban vehicle navigation / Shengfeng Gu in Journal of geodesy, vol 95 n° 6 (June 2021)
[article]
Titre : Multi-GNSS PPP/INS tightly coupled integration with atmospheric augmentation and its application in urban vehicle navigation Type de document : Article/Communication Auteurs : Shengfeng Gu, Auteur ; Chunqi Dai, Auteur ; Wentao Fang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 64 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement
[Termes IGN] centrale inertielle
[Termes IGN] correction atmosphérique
[Termes IGN] couplage GNSS-INS
[Termes IGN] milieu urbain
[Termes IGN] navigation automobile
[Termes IGN] positionnement ponctuel précis
[Termes IGN] retard ionosphèrique
[Termes IGN] teneur verticale totale en électrons
[Termes IGN] Wuhan (Chine)Résumé : (auteur) Precise point positioning (PPP) is receiving increasing interest due to its cost-effectiveness, global coverage and high accuracy. However, its application in the urban environment is still full of challenges due to the satellite tracking sky-view. Thus, we presented a comprehensive positioning model by fusing the multi-GNSS (global navigation satellite system) combination, GNSS/INS (inertial navigation system) tightly coupled integration as well as the ionospheric and tropospheric augmentation in the undifferenced and uncombined PPP. The performance of this model in dual-frequency and single-frequency positioning was assessed with two experiments that denoted as T019 and T023, respectively, and both the experiments were carried out in a real urban environment. Particularly, the experiment T023 was carried out in the Second Ring Road of Wuhan city, which can be regarded as a typical downtown environment. Concerning the regional atmospheric augmentation, observations from 5 reference stations with an inter-station distance of about 40 km were also collected during the experimental time. The comparison between reference stations suggested that the regional tropospheric model had a precision of better than 0.6 cm in terms of zenith tropospheric delay, while the regional ionospheric model had a precision of around 0.5 total electron content unit in terms of Vertical Total Electron Content. It can be concluded that the GPS-only PPP can be improved significantly for urban vehicle navigation with these techniques, i.e., the multi-GNSS, INS tightly coupled integration and the atmospheric augmentation, through the positioning analysis, while INS tightly coupled integration makes the most contributions under the downtown environment, and the improvement of the regional atmospheric augmentation in single-frequency PPP is more significant since that single frequency is more sensitive to the ionospheric delay. In addition, it is proved that the regional atmospheric augmentation accelerates positioning convergence. The 3D positioning root-mean-square (RMS) with the comprehensive positioning model for dual frequency are 0.22 m and 0.77 m for T019 and T023, respectively. Concerning single-frequency PPP, the 3D RMS is 0.45 m and 1.17 m for T019 and T023, respectively. Moreover, taking the lane-level navigation under the downtown environment of T023 into consideration, we further presented the cumulative frequency of the horizontal positioning error less than 1 m, i.e., P(dN2+dE2−−−−−−−−−√ Numéro de notice : A2021-429 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-021-01514-8 Date de publication en ligne : 26/05/2021 En ligne : https://doi.org/10.1007/s00190-021-01514-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97789
in Journal of geodesy > vol 95 n° 6 (June 2021) . - n° 64[article]An improved computerized ionospheric tomography model fusing 3-D multisource ionospheric data enabled quantifying the evolution of magnetic storm / Jian Kong in IEEE Transactions on geoscience and remote sensing, vol 59 n° 5 (May 2021)PermalinkSAR speckle removal using hybrid frequency modulations / Shuaiqi Liu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 5 (May 2021)PermalinkSNR-based water height retrieval in rivers: Application to high amplitude asymmetric tides in the Garonne river / Pierre Zeiger in Remote sensing, vol 13 n° 9 (May-1 2021)PermalinkA stacked dense denoising–segmentation network for undersampled tomograms and knowledge transfer using synthetic tomograms / Dimitrios Bellos in Machine Vision and Applications, vol 32 n° 3 (May 2021)PermalinkUnsupervised multi-level feature extraction for improvement of hyperspectral classification / Qiaoqiao Sun in Remote sensing, vol 13 n° 8 (April-2 2021)PermalinkAtmospheric correction of Sentinel-3/OLCI data for mapping of suspended particulate matter and chlorophyll-a concentration in Belgian turbid coastal waters / Quinten Vanhellemont in Remote sensing of environment, Vol 256 (April 2020)PermalinkAutomatic atmospheric correction for shortwave hyperspectral remote sensing data using a time-dependent deep neural network / Jian Sun in ISPRS Journal of photogrammetry and remote sensing, vol 174 (April 2021)PermalinkDetecting ground deformation in the built environment using sparse satellite InSAR data with a convolutional neural network / Nantheera Anantrasirichai in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)PermalinkHyperspectral image denoising via clustering-based latent variable in variational Bayesian framework / Peyman Azimpour in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)PermalinkImpact of the third frequency GNSS pseudorange and carrier phase observations on rapid PPP convergences / Jiang Guo in GPS solutions, vol 25 n° 2 (April 2021)Permalink