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Regularized integer least-squares estimation: Tikhonov’s regularization in a weak GNSS model / Zemin Wu in Journal of geodesy, vol 96 n° 4 (April 2022)  

Public [article]  inJournal of geodesy > vol 96 n° 4 (April 2022) . - n° 22 Titre : Regularized integer least-squares estimation: Tikhonov’s regularization in a weak GNSS model Type de document : Article/Communication Auteurs : Zemin Wu, Auteur ; Shaofeng Bian, Auteur Année de publication : 2022 Article en page(s) : n° 22 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] affaiblissement géométrique de la précision [Termes IGN] méthode des moindres carrés [Termes IGN] phase GNSS [Termes IGN] positionnement par GNSS [Termes IGN] régularisation de Tychonoff [Termes IGN] résolution d'ambiguïté Résumé : (auteur) The strength of the GNSS precise positioning model degrades in cases of a lack of visible satellites, poor satellite geometry or uneliminated atmospheric delays. The least-squares solution to a weak GNSS model may be unreliable due to a large mean squared error (MSE). Recent studies have reported that Tikhonov’s regularization can decrease the solution’s MSE and improve the success rate of integer ambiguity resolution (IAR), as long as the regularization matrix (or parameter) is properly selected. However, there are two aspects that remain unclear: (i) the optimal regularization matrix to minimize the MSE and (ii) the IAR performance of the regularization method. This contribution focuses on these two issues. First, the “optimal” Tikhonov’s regularization matrix is derived conditioned on an assumption of prior information of the ambiguity. Second, the regularized integer least-squares (regularized ILS) method is compared with the integer least-squares (ILS) method in view of lattice theory. Theoretical analysis shows that regularized ILS can increase the upper and lower bounds of the success rate and reduce the upper bound of the LLL reduction complexity and the upper bound of the search complexity. Experimental assessment based on real observed GPS data further demonstrates that regularized ILS (i) alleviates the LLL reduction complexity, (ii) reduces the computational complexity of determinate-region ambiguity search, and (iii) improves the ambiguity fixing success rate. Numéro de notice : A2022-262 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-021-01585-7 Date de publication en ligne : 28/03/2022 En ligne : https://doi.org/10.1007/s00190-021-01585-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100251 [article]

Cultivating historical heritage area vitality using urban morphology approach based on big data and machine learning / Jiayu Wu in Computers, Environment and Urban Systems, vol 91 (January 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 91 (January 2022) . - n° 101716 Titre : Cultivating historical heritage area vitality using urban morphology approach based on big data and machine learning Type de document : Article/Communication Auteurs : Jiayu Wu, Auteur ; Yutian Lu, Auteur ; Hei Gao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101716 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] apprentissage automatique [Termes IGN] Chine [Termes IGN] conservation du patrimoine [Termes IGN] distribution spatiale [Termes IGN] données massives [Termes IGN] échantillonnage de données [Termes IGN] Extreme Gradient Machine [Termes IGN] morphologie urbaine [Termes IGN] patrimoine culturel [Termes IGN] planification urbaine [Termes IGN] point d'intérêt [Termes IGN] régularisation de Tychonoff [Termes IGN] variation diurne Résumé : (auteur) The conservation of historical heritage can bring social benefits to cities by promoting community economic development and societal creativity. In the early stages of historical heritage conservation, the focus was on the museum-style concept for individual structures. At present, heritage area vitality is often adopted as a general conservation method to increase the vibrancy of such areas. However, it remains unclear whether urban morphological elements suitable for urban areas can be applied to heritage areas. This study uses ridge regression and LightGBM with multi-source big geospatial data to explore whether urban morphological elements that affect the vitality of heritage and urban areas are consistent or have different spatial distributions and daily variations. From a sample of 12 Chinese cities, our analysis shows the following results. First, factors affecting urban vitality differ from those influencing heritage areas. Second, factors influencing urban and heritage areas' vitality have diurnal variations and differ across cities. The overarching contribution of this study is to propose a quantitative and replicable framework for heritage adaptation, combining urban morphology and vitality measures derived from big geospatial data. This study also extends the understanding of forms of heritage areas and provides theoretical support for heritage conservation, urban construction, and economic development. Numéro de notice : A2022-007 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101716 Date de publication en ligne : 30/09/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101716 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99048 [article]

The method of detection and localization of configuration defects in geodetic networks by means of Tikhonov regularization / Roman Kadaj in Reports on geodesy and geoinformatics, vol 112 n° 1 (December 2021)  

Public [article]  inReports on geodesy and geoinformatics > vol 112 n° 1 (December 2021) . - pp 19 - 25 Titre : The method of detection and localization of configuration defects in geodetic networks by means of Tikhonov regularization Type de document : Article/Communication Auteurs : Roman Kadaj, Auteur Année de publication : 2021 Article en page(s) : pp 19 - 25 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes de référence et réseaux [Termes IGN] détection d'erreur [Termes IGN] matrice inversible [Termes IGN] régularisation de Tychonoff [Termes IGN] réseau géodésique [Termes IGN] valeur aberrante Résumé : (auteur) In adjusted geodetic networks, cases of local configuration defects (defects in the geometric structure of the network due to missing data or errors in point numbering) can be encountered, which lead to the singularity of the normal equation system in the least-squares procedure. Numbering errors in observation sets cause the computer program to define the network geometry incorrectly. Another cause of a defect may be accidental omission of certain data records, causing local indeterminacy or lowering of local reliability rates in a network. Obviously, the problem of a configuration defect may be easily detectable in networks with a small number of points. However, it becomes a real problem in large networks, where manual checking of all data becomes a very expensive task. The paper presents a new strategy for the detection of configuration defects with the use of the Tikhonov regularization method. The method was implemented in 1992 in the GEONET system (www.geonet.net.pl). Numéro de notice : A2021-961 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article DOI : 10.2478/rgg-2021-0004 Date de publication en ligne : 17/12/2021 En ligne : https://doi.org/10.2478/rgg-2021-0004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100113 [article]

ALERT: adversarial learning with expert regularization using Tikhonov operator for missing band reconstruction / Litu Rout in IEEE Transactions on geoscience and remote sensing, vol 58 n° 6 (June 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 6 (June 2020) Titre : ALERT: adversarial learning with expert regularization using Tikhonov operator for missing band reconstruction Type de document : Article/Communication Auteurs : Litu Rout, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage automatique [Termes IGN] apprentissage dirigé [Termes IGN] bande spectrale [Termes IGN] cohérence géométrique [Termes IGN] correction d'image [Termes IGN] dégradation d'image [Termes IGN] image Worldview [Termes IGN] pollution acoustique [Termes IGN] qualité d'image [Termes IGN] régularisation de Tychonoff Résumé : (auteur) The Earth observation using remote sensing is one of the most important technologies to assimilate key attributes about the Earth’s surface. To achieve tangible consequence, the internal building blocks of such a complex system must operate flawlessly. However, due to a dynamically changing environment, degradation in sensor electronics, and extreme weather condition remotely sensed images often miss essential information. As the sensors operate over several years in space the likelihood of sensor degradation persists. This results in commonly observed issues, such as stripe noise, missing partial data, and missing band. Various ground-based solutions have been developed to address these technological bottlenecks individually. In this article, we devise a method, which we call ALERT, to tackle missing band reconstruction. The proposed method reconstructs the missing band with the sole supervision of spectral and spatial priors. We compare the proposed framework with state-of-the-art methods and show compelling improvement both qualitatively and quantitatively. We provide both theoretical and empirical evidence of better performance by regularized adversarial learning as compared to complete supervision. Furthermore, we propose a new residual-dense-block (RDB) module to preserve geometric fidelity and assist in efficient gradient flow. We show that ALERT captures essential features such that the spatial and spectral characteristics of the reconstructed band remains preserved. To critically analyze the generalization we test the performance on two different satellite data sets: Resourcesat-2A and WorldView-2. As per our extensive experimentation, the proposed method achieves 20.72%, 13.81%, 1.05%, 15.91%, and 2.94% improvement in the root mean square error (RMSE), SAM, SSIM, PSNR, and SRE, respectively, over the state-of-the-art model. Numéro de notice : A2020-285 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2963818 Date de publication en ligne : 16/01/2020 En ligne : https://doi.org/10.1109/TGRS.2019.2963818 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95108 [article]

Ionosphere probing with simultaneous GNSS radio occultations / Viet-Cuong Pham in GPS solutions, vol 21 n° 1 (January 2017)  

Public [article]  inGPS solutions > vol 21 n° 1 (January 2017) . - pp 101 - 109 Titre : Ionosphere probing with simultaneous GNSS radio occultations Type de document : Article/Communication Auteurs : Viet-Cuong Pham, Auteur ; Jyh-Ching Juang, Auteur Année de publication : 2017 Article en page(s) : pp 101 - 109 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] données GNSS [Termes IGN] gradient ionosphèrique [Termes IGN] inversion [Termes IGN] occultation du signal [Termes IGN] orbite basse [Termes IGN] régularisation de Tychonoff [Termes IGN] teneur totale en électrons Résumé : (Auteur) Radio occultation (RO) is a powerful technique for providing vertical profiles of refractivity, temperature, pressure, and water vapor of the neutral atmosphere and electron density of the ionosphere. The Abel inversion method which is based on the spherical symmetry assumption has been widely utilized to retrieve electron density profiles (EDPs) from RO measurements, which are available by observing Global Navigation Satellite System (GNSS) satellites from low-earth-orbit satellites. It is well known that the Abel inversion is subject to errors in the presence of ionospheric horizontal gradients. With the arrival of new navigation systems, the opportunities of establishing simultaneous GNSS RO events are increasing. We develop an improved Abel inversion technique that accounts for pairs of simultaneous RO events to relax the spherical symmetry assumption. Through the use of Tikhonov regularization, the problem is formulated so that numerical conditioning is improved and a priori information such as expected electron density, asymmetric factor, and vertical total electron content can be incorporated. Appropriate weighting can be determined to reflect the availability and quality of information. By balancing the reference data and measurements, the method thus paves a way for ionospheric probing in challenging geomagnetic conditions as both the EDP at the intersection and the horizontal gradients are retrieved. Simulation and experimental results are provided to show the effectiveness of the proposed method. The robustness and sensitivity of the proposed method are also assessed. Numéro de notice : A2017-014 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1007/s10291-015-0501-1 Date de publication en ligne : 02/01/2016 En ligne : http://dx.doi./org/10.1007/s10291-015-0501-1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83936 [article]

Deep feature extraction and classification of hyperspectral images based on convolutional neural networks / Yushi Chen in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)  

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Exploiting joint sparsity for pansharpening : the J-SparseFI algorithm / Xiao Xiang Zhu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 5 (May 2016)  

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Reducing errors in the GRACE gravity solutions using regularization / H. Save in Journal of geodesy, vol 86 n° 9 (September 2012)  

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