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Modeling multifrequency GPS multipath fading in land vehicle environments / Vicente Carvalho Lima Filho in GPS solutions, vol 25 n° 1 (January 2021)  

Public [article]  inGPS solutions > vol 25 n° 1 (January 2021) . - 14 p. Titre : Modeling multifrequency GPS multipath fading in land vehicle environments Type de document : Article/Communication Auteurs : Vicente Carvalho Lima Filho, Auteur ; Alison Moraes, Auteur Année de publication : 2021 Article en page(s) : 14 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal [Termes descripteurs IGN] densité de probabilité [Termes descripteurs IGN] interférence [Termes descripteurs IGN] propagation du signal [Termes descripteurs IGN] qualité du signal [Termes descripteurs IGN] signal GPS [Termes descripteurs IGN] simulation de signal [Termes descripteurs IGN] trajet multiple [Termes descripteurs IGN] véhicule Résumé : (auteur) The reliability and performance of GPS receivers depend on the quality of the signal received, which can be largely affected by the interference caused by buildings, trees, and other obstacles. Since obstacles are always present in practical applications, several statistical representations have been developed along the years to measure, predict, and compensate errors induced by interferences. Two of the most used models to characterize GPS signal fading are the Nakagami-m and Rice, but in this work, we present evidence that supports the κ–μ distribution as the best fit to deal with multifrequency GPS multipath channels inside urban, rural, and forest areas. A synthetic signal simulator was developed to create propagation cases involving scattering clusters and specular reflections. Additionally, experimental measurements are presented to confirm the κ–μ distribution as the best distribution to characterize different situations on the available three GPS frequencies. We then present typical values of fading coefficients in L1, L2C, and L5 signals, for cases involving urban canyons, regular urban, rural, and dense vegetation areas. These coefficients can also be used to evaluate the receiver performance under similar cases or may be applied in weights measurement methods for positioning computation improvement. Numéro de notice : A2021-002 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-020-01040-8 date de publication en ligne : 09/10/2020 En ligne : https://doi.org/10.1007/s10291-020-01040-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96080 [article]

Heliport detection using artificial neural networks / Emre Baseski in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 9 (September 2020)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 9 (September 2020) . - pp 541-546 Titre : Heliport detection using artificial neural networks Type de document : Article/Communication Auteurs : Emre Baseski, Auteur Année de publication : 2020 Article en page(s) : pp 541-546 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] hélicoptère [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] zone militaire Résumé : (Auteur) Automatic image exploitation is a critical technology for quick content analysis of high-resolution remote sensing images. The presence of a heliport on an image usually implies an important facility, such as military facilities. Therefore, detection of heliports can reveal critical information about the content of an image. In this article, two learning-based algorithms are presented that make use of artificial neural networks to detect H-shaped, light-colored heliports. The first algorithm is based on shape analysis of the heliport candidate segments using classical artificial neural networks. The second algorithm uses deep-learning techniques. While deep learning can solve difficult problems successfully, classical-learning approaches can be tuned easily to obtain fast and reasonable results. Therefore, although the main objective of this article is heliport detection, it also compares a deep-learning based approach with a classical learning-based approach and discusses advantages and disadvantages of both techniques. Numéro de notice : A2020-439 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.9.541 date de publication en ligne : 01/09/2020 En ligne : https://doi.org/10.14358/PERS.86.9.541 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95929 [article]

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Semi-automated framework for generating cycling lane centerlines on roads with roadside barriers from noisy MLS data / Yang Ma in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 167 (September 2020) . - pp 396 - 417 Titre : Semi-automated framework for generating cycling lane centerlines on roads with roadside barriers from noisy MLS data Type de document : Article/Communication Auteurs : Yang Ma, Auteur ; Yubing Zheng, Auteur ; Said Easa, Auteur ; Jianchuan Cheng, Auteur Année de publication : 2020 Article en page(s) : pp 396 - 417 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] axe médian [Termes descripteurs IGN] bicyclette [Termes descripteurs IGN] classification par nuées dynamiques [Termes descripteurs IGN] classification semi-dirigée [Termes descripteurs IGN] numérisation automatique [Termes descripteurs IGN] rastérisation [Termes descripteurs IGN] sécurité routière [Termes descripteurs IGN] segmentation [Termes descripteurs IGN] télémétrie laser mobile [Termes descripteurs IGN] tracé routier Résumé : (auteur) Cycling lane centerlines (CLC) play an important role in the evaluation of safety-related conditions and guiding systems for cyclists along road corridors. The unavailability of design files or undocumented changes in the road infrastructures after improvements has created great difficulty in delineating CLC on existing roads. In this study, mobile laser scanning (MLS) data are introduced into this domain and a four-step semi-automated framework is proposed for generating CLC on roads with roadside barriers (RB). First, MLS data are restructured into the aligned scan-pattern grid using the mapping trajectory data. Second, a rasterization-based clustering approach is applied to segment the off-ground objects from the reorganized MLS data. Third, the RB amongst the segmented objects are identified using a sequential application of the k-Means clustering method and the proposed unidirectional growing method. Finally, the moving average technique and natural cubic spline are applied to generate CLC from the critical positions alongside the identified RB. Testing on three road sections with different types of RB demonstrated that the developed framework can successfully generate CLC from MLS data in the presence of considerable noises. The results also show that the proposed procedure shows better accuracy performance on processing roads with wide RB than a road with narrow RB. Numéro de notice : A2020-550 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.07.009 date de publication en ligne : 04/08/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.07.009 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95779 [article]

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Ship detection in SAR images via local contrast of Fisher vectors / Xueqian Wang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 9 (September 2020) . - pp 6467 - 6479 Titre : Ship detection in SAR images via local contrast of Fisher vectors Type de document : Article/Communication Auteurs : Xueqian Wang, Auteur ; Gang Li, Auteur ; Xiao-Ping Zhang, Auteur ; You He, Auteur Année de publication : 2020 Article en page(s) : pp 6467 - 6479 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] algorithme de superpixels [Termes descripteurs IGN] contraste local [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] détection de cible [Termes descripteurs IGN] distribution de Fisher [Termes descripteurs IGN] fouillis d'échos [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] navire [Termes descripteurs IGN] processus gaussien [Termes descripteurs IGN] rapport signal sur bruit Résumé : (auteur) Existing superpixel-based detection algorithms for ship targets in synthetic aperture radar (SAR) images are often derived from the local contrast of intensities (i.e., the local contrast of the first-order information of superpixels) leading to deteriorating performance in low signal-to-clutter ratio (SCR) cases due to the low contrast between the intensities of targets and the clutter. In this article, we propose a new superpixel-based detector to improve the performance of ship target detection in SAR images via the local contrast of fisher vectors (LCFVs). The new LCFV-based detector exploits multiorder features of the superpixels based on the Gaussian mixture model (GMM) and accordingly improves the discrimination capability between the ship targets and the sea clutter, especially in low SCR cases. Experimental results demonstrate that the proposed LCFV-based detection algorithm provides better detection performance than the commonly used detection algorithms. Numéro de notice : A2020-530 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2976880 date de publication en ligne : 18/03/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2976880 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95713 [article]

Vehicle detection of multi-source remote sensing data using active fine-tuning network / Xin Wu in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 167 (September 2020) . - pp 39 - 53 Titre : Vehicle detection of multi-source remote sensing data using active fine-tuning network Type de document : Article/Communication Auteurs : Xin Wu, Auteur ; Wei Li, Auteur ; Danfeng Hong, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 39 - 53 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] Allemagne [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] données multisources [Termes descripteurs IGN] étiquette [Termes descripteurs IGN] image aérienne [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] modèle stéréoscopique [Termes descripteurs IGN] segmentation [Termes descripteurs IGN] segmentation sémantique [Termes descripteurs IGN] véhicule Résumé : (auteur) Vehicle detection in remote sensing images has attracted increasing interest in recent years. However, its detection ability is limited due to lack of well-annotated samples, especially in densely crowded scenes. Furthermore, since a list of remotely sensed data sources is available, efficient exploitation of useful information from multi-source data for better vehicle detection is challenging. To solve the above issues, a multi-source active fine-tuning vehicle detection (Ms-AFt) framework is proposed, which integrates transfer learning, segmentation, and active classification into a unified framework for auto-labeling and detection. The proposed Ms-AFt employs a fine-tuning network to firstly generate a vehicle training set from an unlabeled dataset. To cope with the diversity of vehicle categories, a multi-source based segmentation branch is then designed to construct additional candidate object sets. The separation of high quality vehicles is realized by a designed attentive classifications network. Finally, all three branches are combined to achieve vehicle detection. Extensive experimental results conducted on two open ISPRS benchmark datasets, namely the Vaihingen village and Potsdam city datasets, demonstrate the superiority and effectiveness of the proposed Ms-AFt for vehicle detection. In addition, the generalization ability of Ms-AFt in dense remote sensing scenes is further verified on stereo aerial imagery of a large camping site. Numéro de notice : A2020-546 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.06.016 date de publication en ligne : 13/07/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.06.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95772 [article]

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Effects of a navigation spoofing signal on a receiver loop and a UAV spoofing approach / Chao Ma in GPS solutions, Vol 24 n° 3 (July 2020)  

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A history of laser scanning, Part 1: space and defense applications / Adam P. Spring in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 7 (July 2020)  

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Extracting commuter-specific destination hotspots from trip destination data – comparing the boro taxi service with Citi Bike in NYC / Andreas Keler in Geo-spatial Information Science, vol 23 n° 2 (June 2020)  

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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)  

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Improving GNSS-acoustic positioning by optimizing the ship’s track lines and observation combinations / Guanxu Chen in Journal of geodesy, vol 94 n° 6 (June 2020)  

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Footprint determination of a spectroradiometer mounted on an unmanned aircraft system / Deepak Gautam in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)  

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A method for urban population density prediction at 30m resolution / Krishnachandran Balakrishnan in Cartography and Geographic Information Science, vol 47 n° 3 (May 2020)  

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Suitable location selection for the electric vehicle fast charging station with AHP and fuzzy AHP methods using GIS / Dogus Guler in Annals of GIS, vol 26 n° 2 (April 2020)  

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Validation of marine geoid models by utilizing hydrodynamic model and shipborne GNSS profiles / Sander Varbla in Marine geodesy, Vol 43 n° 2 (March 2020)  

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Geographies of maritime transport, Ch. 4. Geography versus topology in the evolution of the global container shipping network (1977-2016) / César Ducruet (2020)    

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