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Structure from motion for complex image sets / Mario Michelini in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)
[article]
Titre : Structure from motion for complex image sets Type de document : Article/Communication Auteurs : Mario Michelini, Auteur ; Helmut Mayer, Auteur Année de publication : 2020 Article en page(s) : pp 140 - 152 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement d'images
[Termes IGN] arbre aléatoire minimum
[Termes IGN] caméra numérique
[Termes IGN] distorsion d'image
[Termes IGN] étalonnage d'instrument
[Termes IGN] fusion de données multisource
[Termes IGN] itération
[Termes IGN] jeu de données
[Termes IGN] orientation
[Termes IGN] reconstruction 3D
[Termes IGN] SIFT (algorithme)
[Termes IGN] structure-from-motionRésumé : (auteur) This paper presents an approach for Structure from Motion (SfM) for unorganized complex image sets. To achieve high accuracy and robustness, image triplets are employed and an (approximate) internal camera calibration is assumed to be known. The complexity of an image set is determined by the camera configurations which may include wide as well as weak baselines. Wide baselines occur for instance when terrestrial images and images from small Unmanned Aerial Systems (UAS) are combined. The resulting large (geometric/radiometric) distortions between images make image matching difficult possibly leading to an incomplete result. Weak baselines mean an insufficient distance between cameras compared to the distance of the observed scene and give rise to critical camera configurations. Inappropriate handling of such configurations may lead to various problems in triangulation-based SfM up to total failure. The focus of our approach lies on a complete linking of images even in case of wide or weak baselines. We do not rely on any additional information such as camera configurations, Global Positioning System (GPS) or an Inertial Navigation System (INS). As basis for generating suitable triplets to link the images, an iterative graph-based method is employed formulating image linking as the search for a terminal Steiner minimum tree in the line graph. SIFT (Lowe, 2004) descriptors are embedded into Hamming space for fast image similarity ranking. This is employed to limit the number of pairs to be geometrically verified by a computationally and more complex wide baseline matching method (Mayer et al., 2012). Critical camera configurations which are not suitable for geometric verification are detected by means of classification (Michelini and Mayer, 2019). Additionally, we propose a graph-based approach for the optimization of the hierarchical merging of triplets to efficiently generate larger image subsets. By this means, a complete, 3D reconstruction of the scene is obtained. Experiments demonstrate that the approach is able to produce reliable orientation for large image sets comprising wide as well as weak baseline configurations. Numéro de notice : A2020-355 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.05.020 Date de publication en ligne : 12/06/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.05.020 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95242
in ISPRS Journal of photogrammetry and remote sensing > vol 166 (August 2020) . - pp 140 - 152[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2020081 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020083 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020082 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Trajectory drift–compensated solution of a stereo RGB-D mapping system / Shengjun Tang in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 6 (June 2020)
[article]
Titre : Trajectory drift–compensated solution of a stereo RGB-D mapping system Type de document : Article/Communication Auteurs : Shengjun Tang, Auteur ; Qing Zhu, Auteur ; You Li, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 359 - 372 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] compensation
[Termes IGN] image RVB
[Termes IGN] itération
[Termes IGN] optimisation (mathématiques)
[Termes IGN] reconstruction 3DRésumé : (Auteur) Multiple sensors are commonly used for three-dimensional (3D)-mapping or robotic-vision applications, as they provide a larger field of view and sufficient observations to fulfill frame-registration and map-updating tasks. However, the data sequences generated by multiple sensors can be inconsistent and contain significant time drift. In this paper, we describe the trajectory drift–compensated strategy that we designed to eliminate the influence of time drift between sensors, remove the inconsistency between the sequences from various sensors, and thereby generate a coarse-to-fine procedure for robust camera-tracking based on two-dimensional–3D observations from stereo sensors. We present the mathematical analysis of the iterative optimizations for pose tracking in a stereo red, green, blue plus depth (RGB-D) camera. Finally, complex indoor scenario experiments demonstrate the efficiency of the proposed stereo RGB-D simultaneous localization and mapping solution. The results verify that the proposed stereo RGB-D mapping solution effectively improves the accuracies of both camera-tracking and 3D reconstruction. Numéro de notice : A2020-241 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.6.359 Date de publication en ligne : 01/06/2020 En ligne : https://doi.org/10.14358/PERS.86.6.359 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95199
in Photogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 6 (June 2020) . - pp 359 - 372[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2020061 SL Revue Centre de documentation Revues en salle Disponible Filtering of airborne LiDAR bathymetry based on bidirectional cloth simulation / Anxiu Yang in ISPRS Journal of photogrammetry and remote sensing, vol 163 (May 2020)
[article]
Titre : Filtering of airborne LiDAR bathymetry based on bidirectional cloth simulation Type de document : Article/Communication Auteurs : Anxiu Yang, Auteur ; Fanlin Yang, Auteur ; Dianpeng Su, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 49 - 61 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] ajustement de paramètres
[Termes IGN] Chine
[Termes IGN] courbe de Gauss
[Termes IGN] données lidar
[Termes IGN] filtrage de points
[Termes IGN] itération
[Termes IGN] lidar bathymétrique
[Termes IGN] relief sous-marin
[Termes IGN] semis de points
[Termes IGN] télémétrie laser aéroportéRésumé : (auteur) Current filtering methods of airborne LiDAR bathymetry (ALB) point clouds cannot identify negative anomalies or avoid over-filtering of the data. To overcome these problems, we propose a bidirectional cloth simulation filtering (BCSF) method and verify it using captured data. First, a transfer iterative trend surface is established to eliminate the negative anomalies and realize the continuous expression of the seafloor topography. The terrain complexities of the seafloor points are calculated using four extracted feature factors: slope, standard deviation of depth, Gaussian curvature, and roughness. We then calculate the sub-regional terrain complexity and the adaptive distance threshold and obtain user-defined parameters. Finally, sub-regional filtering is carried out, and a filtered surface is established to solve the over-filtering problem of convex and concave seafloor topographies based on the BCSF correction model. To evaluate the performance of the proposed method, the BCSF method was applied to ALB data captured around Yuanzhi Island in the South China Sea. The experimental results show that the BCSF method effectively filters out non-seafloor points and fully preserves the seafloor microtopography to realize the integrity of the seafloor topography. The proposed BCSF method outperforms the cloth simulation filtering method in terms of the elimination rate, which decreases from 38.78% to 2.52% and from 29.52% to 0.70% in the whole study area and local study area, respectively. Consequently, the BCSF method that combines forward filtering with inverse filtering exhibits complementary advantages, avoids over-filtering, and demonstrates strong adaptability and robustness for ALB data. Numéro de notice : A2020-137 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.03.004 Date de publication en ligne : 09/03/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.03.004 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94755
in ISPRS Journal of photogrammetry and remote sensing > vol 163 (May 2020) . - pp 49 - 61[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2020051 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020053 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020052 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Multiscale Intensity Propagation to Remove Multiplicative Stripe Noise From Remote Sensing Images / Hao Cui in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)
[article]
Titre : Multiscale Intensity Propagation to Remove Multiplicative Stripe Noise From Remote Sensing Images Type de document : Article/Communication Auteurs : Hao Cui, Auteur ; Peng Jia, Auteur ; Guo Zhang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 2308 - 2323 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des correspondances
[Termes IGN] bande spectrale
[Termes IGN] capteur à balayage
[Termes IGN] correction d'image
[Termes IGN] dégradation d'image
[Termes IGN] délignage
[Termes IGN] filtrage du bruit
[Termes IGN] filtrage du rayonnement
[Termes IGN] image hyperspectrale
[Termes IGN] intensité lumineuse
[Termes IGN] itération
[Termes IGN] méthode robuste
[Termes IGN] pollution acoustiqueRésumé : (auteur) Sensor instability, dark currents, and other factors often cause stripe noise corruption in hyperspectral remote sensing images and severely limit their application in practical purposes. Previous studies have proposed numerous destriping algorithms that have yielded impressive results. Although most destriping algorithms are based on the premise of additive noise, a few studies have focused directly on multiplicative stripe noise. This article fully analyzes the characteristics of the stripe noise of OHS-01 images and proposes a multiplicative stripe noise removal method. Specifically, stripe noise is tackled by performing radiometric normalization of different columns in the image. First, the relative gain coefficients of adjacent columns are separated based on prior knowledge. Second, the local relative intensity correspondence of the image columns are established by means of intensity propagation, intensity connection, and so on. Finally, the above-mentioned process is iterated in multiscale space, and the accumulated gain correction coefficient maps were used to correct the radiation of the original image. The results of extensive experiments on simulated and real remote sensing image data demonstrate that the proposed method can, in most cases, yield desirable results. In certain cases, the results are even better, visually, and quantitatively, than those obtained using classical algorithms. Moreover, the proposed method has high robustness and efficiency. Thus, it can conform to the requirements of engineering applications. Numéro de notice : A2020-194 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2947599 Date de publication en ligne : 12/11/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2947599 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94861
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 4 (April 2020) . - pp 2308 - 2323[article]Online flu epidemiological deep modeling on disease contact network / Liang Zhao in Geoinformatica, vol 24 n° 2 (April 2020)
[article]
Titre : Online flu epidemiological deep modeling on disease contact network Type de document : Article/Communication Auteurs : Liang Zhao, Auteur ; Jiangzhuo Chen, Auteur ; Feng Chen, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 443 – 475 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] classification par réseau neuronal
[Termes IGN] classification semi-dirigée
[Termes IGN] données localisées des bénévoles
[Termes IGN] épidémie
[Termes IGN] itération
[Termes IGN] maladie infectieuse
[Termes IGN] maladie virale
[Termes IGN] modélisation
[Termes IGN] optimisation (mathématiques)
[Termes IGN] réseau social
[Termes IGN] surveillance sanitaireRésumé : (auteur) The surveillance and preventions of infectious disease epidemics such as influenza and Ebola are important and challenging issues. It is therefore crucial to characterize the disease progress and epidemics process efficiently and accurately. Computational epidemiology can model the progression of the disease and its underlying contact network, but as yet lacks the ability to process of real-time and fine-grained surveillance data. Social media, on the other hand, provides timely and detailed disease surveillance but is insensible to the underlying contact network and disease model. To address these challenges simultaneously, this paper proposes a novel semi-supervised neural network framework that integrates the strengths of computational epidemiology and social media mining techniques for influenza epidemiological modeling. Specifically, this framework learns social media users’ health states and intervention actions in real time, regularized by the underlying disease model and contact network. The learned knowledge from social media can then be fed into the computational epidemic model to improve the efficiency and accuracy of disease diffusion modeling. We propose an online optimization algorithm that iteratively processes the above interactive learning process. he extensive experimental results provided demonstrated that our approach can not only outperform competing methods by a substantial margin in forecasting disease outbreaks, but also characterize the individual-level disease progress and diffusion effectively and efficiently. Numéro de notice : A2020-359 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-019-00376-9 Date de publication en ligne : 25/07/2019 En ligne : https://doi.org/10.1007/s10707-019-00376-9 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95264
in Geoinformatica > vol 24 n° 2 (April 2020) . - pp 443 – 475[article]Progress towards a rigorous error propagation for total least-squares estimates / Burkhard Schaffrin in Journal of applied geodesy, vol 14 n° 2 (April 2020)PermalinkReducing multipath effect of low-cost GNSS receivers for monitoring by considering temporal correlations / Li Zhang in Journal of applied geodesy, vol 14 n° 2 (April 2020)PermalinkSimultaneous intensity bias estimation and stripe noise removal in infrared images using the global and local sparsity constraints / Li Liu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)PermalinkMapping precipitable water vapor time series from Sentinel-1 interferometric SAR / Pedro Mateus in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)PermalinkMulti-spectral image change detection based on single-band iterative weighting and fuzzy C-means clustering / Liyuan Ma in European journal of remote sensing, vol 53 n° 1 (2020)PermalinkLa biodiversité à l’épreuve des choix d’aménagement : une approche par la modélisation appliquée à la Région Occitanie / Coralie Calvet in Sciences, eaux & territoires, n° 31 (janvier 2020)Permalink3D iterative spatiotemporal filtering for classification of multitemporal satellite data sets / Hessah Albanwan in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 1 (January 2020)PermalinkApplying iterative method to solving high-order terms of seafloor topography / Diao Fan in Marine geodesy, Vol 43 n° 1 (January 2020)PermalinkGlobal iterative geometric calibration of a linear optical satellite based on sparse GCPs / Yingdong Pi in IEEE Transactions on geoscience and remote sensing, vol 58 n° 1 (January 2020)PermalinkA new segmentation method for the homogenisation of GNSS-derived IWV time-series / Annarosa Quarello (2020)PermalinkSmoothing algorithms for navigation, localisation and mapping based on high-grade inertial sensors / Paul Chauchat (2020)PermalinkAutomatic determination of stream networks from DEMs by using road network data to locate culverts / Ville Mäkinen in International journal of geographical information science IJGIS, Vol 33 n° 1-2 (January - February 2019)PermalinkBayesian iterative reconstruction methods for 3D X-ray Computed Tomography / Camille Chapdelaine (2019)PermalinkPermalinkMeasuring stem diameters with TLS in boreal forests by complementary fitting procedure / Timo P Pitkänen in ISPRS Journal of photogrammetry and remote sensing, vol 147 (January 2019)PermalinkSimultaneous chain-forming and generalization of road networks / Susanne Wenzel in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 1 (January 2019)PermalinkAutomatic building rooftop extraction from aerial images via hierarchical RGB-D priors / Shibiao Xu in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)PermalinkSecond iteration of photogrammetric processing to refine image orientation with improved tie-points / Truong Giang Nguyen in Sensors, vol 18 n° 7 (July 2018)PermalinkA typification method for linear pattern in urban building generalisation / Xianyong Gong in Geocarto international, vol 33 n° 2 (February 2018)PermalinkDeep learning based vehicular mobility models for intelligent transportation systems / Jian Zhang (2018)Permalink