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Stochastic modeling for VRS network-based GNSS RTK with residual interpolation uncertainty / Thanate Jongrujinan in Journal of applied geodesy, vol 14 n° 3 (July 2020)  

Public [article]  inJournal of applied geodesy > vol 14 n° 3 (July 2020) . - pp 317 – 325 Titre : Stochastic modeling for VRS network-based GNSS RTK with residual interpolation uncertainty Type de document : Article/Communication Auteurs : Thanate Jongrujinan, Auteur ; Chalermchon Satirapod, Auteur Année de publication : 2020 Article en page(s) : pp 317 – 325 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] correction atmosphérique [Termes descripteurs IGN] incertitude de position [Termes descripteurs IGN] interpolation [Termes descripteurs IGN] matrice de covariance [Termes descripteurs IGN] modèle stochastique [Termes descripteurs IGN] positionnement cinématique en temps réel [Termes descripteurs IGN] positionnement par GNSS [Termes descripteurs IGN] précision du positionnement [Termes descripteurs IGN] résolution d'ambiguïté [Termes descripteurs IGN] station virtuelle de référence [Vedettes matières IGN] Traitement de données GNSS Résumé : (auteur) The key concept of the virtual reference station (VRS) network-based technique is to use the observables of multiple reference stations to generate the network corrections in the form of a virtual reference station at a nearby user’s location. Regarding the expected positioning accuracy, the novice GNSS data processing strategies have been adopted in the server-side functional model for mitigating distance-dependent errors including atmospheric effects and orbital uncertainty in order to generate high-quality virtual reference stations. In addition, the realistic stochastic model also plays an important role to take account of the unmodelled error in the rover-side processing. The results of our previous study revealed that the minimum norm quadratic unbiased estimation (MINQUE) stochastic model procedure can improve baseline component accuracy and integer ambiguity reliability, however, it requires adequate epoch length in a solution to calculate the elements of the variance-covariance matrix. As a result, it may not be suitable for urban environment where the satellite signal interruptions take place frequently, therefore, the ambiguity resolution needs to be resolved within the limited epochs. In order to address this limitation, this study proposed the stochastic model based on using the residual interpolation uncertainty (RIU) as the weighting schemes. This indicator reflects the quality of network corrections for any satellite pair at a specific rover position and can be calculated on the epoch-by-epoch basis. The comparison results with the standard stochastic model indicated that the RIU-weight model produced slightly better positioning accuracy but increased significant level of the ambiguity resolution successful rate. Numéro de notice : A2020-398 Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jag-2020-0007 date de publication en ligne : 10/04/2020 En ligne : https://doi.org/10.1515/jag-2020-0007 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95433 [article]

A point cloud feature regularization method by fusing judge criterion of field force / Xijiang Chen in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 5 (May 2020) . - pp 2994 - 3006 Titre : A point cloud feature regularization method by fusing judge criterion of field force Type de document : Article/Communication Auteurs : Xijiang Chen, Auteur ; Qing Liu, Auteur ; Keguen Yu, Auteur Année de publication : 2020 Article en page(s) : pp 2994 - 3006 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] analyse vectorielle [Termes descripteurs IGN] détection de contours [Termes descripteurs IGN] échantillonnage [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] matrice de covariance [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] modélisation du bâti [Termes descripteurs IGN] niveau de gris (image) [Termes descripteurs IGN] partitionnement binaire [Termes descripteurs IGN] plus proche voisin (algorithme) [Termes descripteurs IGN] reconstruction d'objet [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] spline cubique [Termes descripteurs IGN] traitement d'image [Termes descripteurs IGN] transformation de Hough [Termes descripteurs IGN] Wuhan (Chine) Résumé : (auteur) Point cloud boundary is an important part of the surface model. The traditional feature extraction method has slow speed and low efficiency and only achieves the boundary feature points. Hence, the point cloud feature regularization is proposed to obtain the boundary lines based on the fast extraction of feature points in this article. First, an improved $k$ - $d$ tree method is used to search the $k$ neighbors of sampling point. Then, the sampling point and its $k$ neighbors are used as the reference points set to fit a microcut plane and project to the plane. The local coordinate system is established on the microcut plane to convert 3-D into 2-D. The boundary feature points are identified by judging criterion of field force and then are sorted and connected according to the vector deflected angle and distance. Finally, the boundary lines are smoothed by the improved cubic B-spline fitting method. Experiments show that the proposed method can extract the boundary feature points quickly and efficiently, and the mean error of boundary lines is 0.0674 mm and the standard deviation is 0.0346 mm, which has high precision. This proposed method was also successfully applied to feature extraction and boundary fitting of Xinyi teaching building of the Wuhan University of Technology. Numéro de notice : A2020-230 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2946326 date de publication en ligne : 16/12/2020 En ligne : https://doi.org/10.1109/TGRS.2019.2946326 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94968 [article]

Deep SAR-Net: learning objects from signals / Zhongling Huang in ISPRS Journal of photogrammetry and remote sensing, vol 161 (March 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 161 (March 2020) . - pp 179 - 193 Titre : Deep SAR-Net: learning objects from signals Type de document : Article/Communication Auteurs : Zhongling Huang, Auteur ; Mihai Datcu, Auteur ; Zongxu Pan, Auteur ; Bin Lei, Auteur Année de publication : 2020 Article en page(s) : pp 179 - 193 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] image Terra [Termes descripteurs IGN] matrice de covariance [Termes descripteurs IGN] micro-onde [Termes descripteurs IGN] polarisation [Termes descripteurs IGN] temps-fréquence Résumé : (Auteur) This paper introduces a novel Synthetic Aperture Radar (SAR) specific deep learning framework for complex-valued SAR images. The conventional deep convolutional neural networks based methods usually take the amplitude information of single-polarization SAR images as the input to learn hierarchical spatial features automatically, which may have difficulties in discriminating objects with similar texture but discriminative scattering patterns. Our novel deep learning framework, Deep SAR-Net, takes complex-valued SAR images into consideration to learn both spatial texture information and backscattering patterns of objects on the ground. On the one hand, we transfer the detected SAR images pre-trained layers to extract spatial features from intensity images. On the other hand, we dig into the Fourier domain to learn physical properties of the objects by joint time-frequency analysis on complex-valued SAR images. We evaluate the effectiveness of Deep SAR-Net on three complex-valued SAR datasets from Sentinel-1 and TerraSAR-X satellite and demonstrate how it works better than conventional deep CNNs, especially on man-made objects classes. The proposed datasets and the trained Deep SAR-Net model with all codes are provided. Numéro de notice : A2020-065 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.01.016 date de publication en ligne : 23/01/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.01.016 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94583 [article]

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Identification of alpine glaciers in the central Himalayas using fully polarimetric L-Band SAR data / Guo-Hui Yao in IEEE Transactions on geoscience and remote sensing, vol 58 n° 1 (January 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 1 (January 2020) . - pp 691 - 703 Titre : Identification of alpine glaciers in the central Himalayas using fully polarimetric L-Band SAR data Type de document : Article/Communication Auteurs : Guo-Hui Yao, Auteur ; Chang-qing Ke, Auteur ; Xiaobing Zhou, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 691 - 703 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] analyse multiéchelle [Termes descripteurs IGN] bande L [Termes descripteurs IGN] classification orientée objet [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] données polarimétriques [Termes descripteurs IGN] échantillonnage [Termes descripteurs IGN] glacier [Termes descripteurs IGN] Himalaya [Termes descripteurs IGN] image ALOS-PALSAR [Termes descripteurs IGN] image Landsat-OLI [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] interferométrie différentielle [Termes descripteurs IGN] matrice de covariance [Termes descripteurs IGN] précision de la classification [Termes descripteurs IGN] segmentation Résumé : (auteur) To study the applicability of full polarimetric synthetic aperture radar (SAR) data to identify alpine glaciers in the central Himalayas, six polarimetric decomposition methods were used to obtain 20 polarimetric characteristic parameters based on the Advanced Land Observing Satellite 2 (ALOS-2) Phased Array type L-band SAR (PALSAR) data. Object-oriented multiscale segmentation was performed on a Landsat 8 Operational Land Imager (OLI) image prior to classification, and the vector boundaries of different types of training samples were selected from the segmented results. We performed a support vector machine (SVM)-based classification on the characteristic parameters from each polarimetric decomposition. All 20 parameters were then screened and combined according to different requirements: the degree of separability of different types of training samples and the type of scattering mechanisms. The results show that the classification accuracy of the incoherent decomposition characteristics based on the covariance matrix is the best, reaching 87%, and it can exceed 91% after adding the local incidence angle to the suite of classifiers. Eventually, more than 93% accuracy was achieved using a combination of multiple polarimetric parameters, which reduced the misclassification between bare ice and rock. We also analyzed the use of controlling factors on the accuracy of alpine glacier identification and found that the polarimetric information and aspect of the glacier surface are the most important factors. The former is the main basis for identification but the latter will confuse the feature distributions of different categories and cause misclassification. Numéro de notice : A2020-077 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2939430 date de publication en ligne : 25/09/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2939430 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94613 [article]

Robust M–M unscented Kalman filtering for GPS/IMU navigation / Cheng Yang in Journal of geodesy, vol 93 n° 8 (August 2019)  

Public [article]  inJournal of geodesy > vol 93 n° 8 (August 2019) . - pp 1093 - 1104 Titre : Robust M–M unscented Kalman filtering for GPS/IMU navigation Type de document : Article/Communication Auteurs : Cheng Yang, Auteur ; Wenzhong Shi, Auteur ; Wu Chen, Auteur Année de publication : 2019 Article en page(s) : pp 1093 - 1104 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement [Termes descripteurs IGN] coefficient de corrélation [Termes descripteurs IGN] erreur de mesure [Termes descripteurs IGN] erreur de modèle [Termes descripteurs IGN] filtre de Kalman [Termes descripteurs IGN] GNSS-INS [Termes descripteurs IGN] matrice de covariance [Termes descripteurs IGN] méthode du maximum de vraisemblance (estimation) [Termes descripteurs IGN] méthode robuste [Termes descripteurs IGN] modèle non linéaire Résumé : (auteur) In this paper, a robust unscented Kalman filter (UKF) based on the generalized maximum likelihood estimation (M-estimation) is proposed to improve the robustness of the integrated navigation system of Global Navigation Satellite System and Inertial Measurement Unit. The UKF is a variation of Kalman filter by which the Jacobian matrix calculation in a nonlinear system state model is not necessary. The proposed robust M–M unscented Kalman filter (RMUKF) applies the M-estimation principle to both functional model errors and measurement errors. Hence, this robust filter attenuates the influences of disturbances in the dynamic model and of measurement outliers without linearizing the nonlinear state space model. In addition, an equivalent weight matrix, composed of the bi-factor shrink elements, is proposed in order to keep the original correlation coefficients of the predicted state unchanged. Furthermore, a nonlinear error model is used as the dynamic equation to verify the performance of the proposed RMUKF with a simulation and field test. Compared with the conventional UKF, the impacts of measurement outliers and system disturbances on the state estimation are both controlled by RMUKF. Numéro de notice : A2019-370 Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-018-01227-5 date de publication en ligne : 22/01/2019 En ligne : https://doi.org/10.1007/s00190-018-01227-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93455 [article]

An improved robust Kalman filtering strategy for GNSS kinematic positioning considering small cycle slips / Wanke Liu in Advances in space research, vol 63 n° 9 (1 May 2019)  

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On constrained integrated total Kalman filter for integrated direct geo-referencing / Vahid Mahboub in Survey review, vol 51 n° 364 (January 2019)  

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Pointwise approach for texture analysis and characterization from very high resolution remote sensing images / Minh-Tan Pham (2019)  

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Validating and comparing GNSS antenna calibrations / Ulla Kallio in Journal of geodesy, vol 93 n° 1 (January 2019)  

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Atmospheric artifacts correction with a covariance-weighted linear model over mountainous regions / Zhongbo Hu in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)  

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Remote sensing scene classification using multilayer stacked covariance pooling / Nanjun He in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)  

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Towards operational marker-free registration of terrestrial lidar data in forests / Jean-François Tremblay in ISPRS Journal of photogrammetry and remote sensing, vol 146 (December 2018)  

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Least-squares cross-wavelet analysis and its applications in geophysical time series / Ebrahim Ghaderpour in Journal of geodesy, vol 92 n° 10 (October 2018)  

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Unmixing polarimetric radar images based on land cover type identified by higher resolution optical data before target decomposition: application to forest and bare soil / Sébastien Giordano in IEEE Transactions on geoscience and remote sensing, vol 56 n° 10 (October 2018)  

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A methodology for least-squares local quasi-geoid modelling using a noisy satellite-only gravity field model / R. Klees in Journal of geodesy, vol 92 n° 4 (April 2018)  

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