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Multipath mitigation for improving GPS narrow-lane uncalibrated phase delay estimation and speeding up PPP ambiguity resolution / Kai Zheng in Measurement, vol 206 (January 2023)
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Titre : Multipath mitigation for improving GPS narrow-lane uncalibrated phase delay estimation and speeding up PPP ambiguity resolution Type de document : Article/Communication Auteurs : Kai Zheng, Auteur ; Lingmin Tan, Auteur ; Kezhong Liu, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 112243 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] analyse en composantes principales
[Termes IGN] atténuation
[Termes IGN] correction du trajet multiple
[Termes IGN] mesurage de pseudo-distance
[Termes IGN] phase GPS
[Termes IGN] positionnement ponctuel précis
[Termes IGN] résolution d'ambiguïté
[Termes IGN] temps de convergence
[Termes IGN] trajet multipleRésumé : (auteur) Precise point positioning (PPP) has been recognized as a powerful tool for various geophysical applications. However, the long convergence time required to resolve a reliable ambiguity impedes its further application in time-critical scenarios. Although PPP ambiguity resolution (AR) can shorten the convergence time, its performance is subject to the quality of float ambiguity estimates and the uncalibrated phase delay (UPD), which can be contaminated by multipath errors. Furthermore, the observation residuals derived from PPP are very likely to be affected by the common-mode error (CME), thereby deteriorating the multipath modeling accuracy. The principal component analysis (PCA) is employed to mitigate the CME effect, and the multipath is modeled using a multipath hemispherical map (MHM). Consequently, the narrow-lane (NL) UPDs with multipath correction have better temporal stability and residual distributions than those without correction. Compared with sidereal filtering (SF), the MHM0.5 has comparable residual variance reduction percentages, indicating its capability of capturing high-frequency multipath. For static PPP AR, the averaged time to first fix (TTFF) can be reduced by 24.2% to about 26 min and the convergence time can be achieved within 16.2 min after multipath correction. The pseudorange multipath correction significantly contributes to shortening the TTFF and convergence time. Reducing the resolution of MHM increases the risk of extending the TTFF. For kinematic PPP AR with MHM0.5, the convergence time exhibits a remarkable improvement when compared with that of the uncorrected case (21.7 min versus 40.2 min), and 20% of the stations achieve convergence within 10 min. Meanwhile, a few stations only take one minute to achieve convergence. The contribution of the multipath correction to the fixing rate is comparatively small. After applying MHM0.5, the kinematic positioning accuracies are improved by 35.7%, 12.6%, and 24.4% to 1.26, 1.39, and 2.73 cm for the east, north, and up components, respectively. Numéro de notice : A2023-027 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1016/j.measurement.2022.112243 Date de publication en ligne : 24/11/2022 En ligne : https://doi.org/10.1016/j.measurement.2022.112243 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102267
in Measurement > vol 206 (January 2023) . - n° 112243[article]Prototype-guided multitask adversarial network for cross-domain LiDAR point clouds semantic segmentation / Zhimin Yuan in IEEE Transactions on geoscience and remote sensing, vol 61 n° 1 (January 2023)
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Titre : Prototype-guided multitask adversarial network for cross-domain LiDAR point clouds semantic segmentation Type de document : Article/Communication Auteurs : Zhimin Yuan, Auteur ; Ming Cheng, Auteur ; Wankang Zeng, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 5700613 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] alignement des données
[Termes IGN] apprentissage non-dirigé
[Termes IGN] compression de données
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] réseau antagoniste génératif
[Termes IGN] segmentation sémantique
[Termes IGN] semis de pointsRésumé : (auteur) Unsupervised domain adaptation (UDA) segmentation aims to leverage labeled source data to make accurate predictions on unlabeled target data. The key is to make the segmentation network learn domain-invariant representations. In this work, we propose a prototype-guided multitask adversarial network (PMAN) to achieve this. First, we propose an intensity-aware segmentation network (IAS-Net) that leverages the private intensity information of target data to substantially facilitate feature learning of the target domain. Second, the category-level cross-domain feature alignment strategy is introduced to flee the side effects of global feature alignment. It employs the prototype (class centroid) and includes two essential operations: 1) build an auxiliary nonparametric classifier to evaluate the semantic alignment degree of each point based on the prediction consistency between the main and auxiliary classifiers and 2) introduce two class-conditional point-to-prototype learning objectives for better alignment. One is to explicitly perform category-level feature alignment in a progressive manner, and the other aims to shape the source feature representation to be discriminative. Extensive experiments reveal that our PMAN outperforms state-of-the-art results on two benchmark datasets. Numéro de notice : A2023-118 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2023.3234542 Date de publication en ligne : 05/01/2023 En ligne : https://doi.org/10.1109/TGRS.2023.3234542 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102489
in IEEE Transactions on geoscience and remote sensing > vol 61 n° 1 (January 2023) . - n° 5700613[article]Semi-supervised label propagation for multi-source remote sensing image change detection / Fan Hao in Computers & geosciences, vol 170 (January 2023)
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Titre : Semi-supervised label propagation for multi-source remote sensing image change detection Type de document : Article/Communication Auteurs : Fan Hao, Auteur ; Zong-Fang Ma, Auteur ; Hong Peng Tian, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 105249 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de groupement
[Termes IGN] classification barycentrique
[Termes IGN] classification pixellaire
[Termes IGN] détection de changement
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] étiquette
[Termes IGN] filtrage du bruit
[Termes IGN] image multi sourcesRésumé : (auteur) Remote sensing image change detection remains a challenging task. Most existing approaches are based on fully supervised learning, but labeled data are so scarce for change detection. It is difficult to exhibit high detection performance with a limited amount of labeled data. In this paper, we propose a semi-supervised Label Propagation (SSLP) approach for multi-source remote sensing image change detection. First, a clustering label propagation (CLP) method is designed to cluster pre and post images, respectively, and assign pseudo labels to unlabeled pixel pairs that have similar mapping relationships to labeled pixel pairs. Second, a pixel density metric is investigated to filter out the data with low density and retain the data with high density, which can ensure the reliability of the propagated data. Third, a secondary expansion method based on pixel neighborhood is used to generate enough training data for training a classifier. Finally, the effectiveness of SSLP is validated on three real datasets by comparing to other related methods. Numéro de notice : A2023-032 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.cageo.2022.105249 Date de publication en ligne : 19/10/2022 En ligne : https://doi.org/10.1016/j.cageo.2022.105249 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102292
in Computers & geosciences > vol 170 (January 2023) . - n° 105249[article]Solid waste mapping based on very high resolution remote sensing imagery and a novel deep learning approach / Bowen Niu in Geocarto international, vol 38 n° 1 ([01/01/2023])
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Titre : Solid waste mapping based on very high resolution remote sensing imagery and a novel deep learning approach Type de document : Article/Communication Auteurs : Bowen Niu, Auteur ; Quanlong Feng, Auteur ; Jianyu Yang, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 2164361 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] cartographie thématique
[Termes IGN] Chine
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] contour
[Termes IGN] déchet
[Termes IGN] fusion de données
[Termes IGN] image à très haute résolution
[Termes IGN] Inde
[Termes IGN] Mexique
[Termes IGN] urbanisationRésumé : (auteur) The urbanization worldwide leads to the rapid increase of solid waste, posing a threat to environment and people’s wellbeing. However, it is challenging to detect solid waste sites with high accuracy due to complex landscape, and very few studies considered solid waste mapping across multi-cities and in large areas. To tackle this issue, this study proposes a novel deep learning model for solid waste mapping from very high resolution remote sensing imagery. By integrating a multi-scale dilated convolutional neural network (CNN) and a Swin-Transformer, both local and global features are aggregated. Experiments in China, India and Mexico indicate that the proposed model achieves high performance with an average accuracy of 90.62%. The novelty lies in the fusion of CNN and Transformer for solid waste mapping in multi-cities without the need for pixel-wise labelled data. Future work would consider more sophisticated methods such as semantic segmentation for fine-grained solid waste classification. Numéro de notice : A2023-109 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2022.2164361 Date de publication en ligne : 04/01/2023 En ligne : https://doi.org/10.1080/10106049.2022.2164361 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102407
in Geocarto international > vol 38 n° 1 [01/01/2023] . - n° 2164361[article]Spatial distribution analysis of seismic activity based on GMI, LMI, and LISA in China / Ziyi Cao in Open geosciences, vol 14 n° 1 (January 2023)
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Titre : Spatial distribution analysis of seismic activity based on GMI, LMI, and LISA in China Type de document : Article/Communication Auteurs : Ziyi Cao, Auteur ; Heng Zhang, Auteur ; Yan Liu, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 89 - 97 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de groupement
[Termes IGN] analyse diachronique
[Termes IGN] autocorrélation spatiale
[Termes IGN] Chine
[Termes IGN] distribution spatiale
[Termes IGN] séisme
[Termes IGN] sismicitéRésumé : (auteur) Recently, all kinds of geological disasters happen frequently on the earth. In China, there are countless earthquakes every year, which greatly affect the country’s economic level and development as well as the people’s life and health. The analysis of seismic activity is becoming more and more significant. In this article, the spatial distribution of China’s seismic activities was analyzed by using the provincial seismic data from 1970 to 2013. On the basis of spatial autocorrelation analysis theory, Global Moran’s I, Local Moran’s I, and the Local Indicators of Spatial Association are used to measure the geospatial distribution characteristics of China’s seismic activities. The research results show that earthquakes in mainland China have significant global autocorrelation characteristics as a whole, and the global autocorrelation coefficients are all positive. And the Z-value test (P Numéro de notice : A2023-052 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/POSITIONNEMENT Nature : Article En ligne : https://doi.org/10.1515/geo-2020-0332 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102383
in Open geosciences > vol 14 n° 1 (January 2023) . - pp 89 - 97[article] PermalinkThe cellular automata approach in dynamic modelling of land use change detection and future simulations based on remote sensing data in Lahore Pakistan / Muhammad Nasar Ahmad in Photogrammetric Engineering & Remote Sensing, PERS, vol 89 n° 1 (January 2023)
PermalinkTree species classification in a typical natural secondary forest using UAV-borne LiDAR and hyperspectral data / Ying Quan in GIScience and remote sensing, vol 60 n° 1 (2023)
PermalinkUsing Google Earth Engine to classify unique forest and agroforest classes using a mix of Sentinel 2a spectral data and topographical features: a Sri Lanka case study / W.D.K.V. Nandasena in Geocarto international, vol 38 n° inconnu ([01/01/2023])
PermalinkWavelet-like denoising of GNSS data through machine learning. Application to the time series of the Campi Flegrei volcanic area (Southern Italy) / Rolando Carbonari in Geomatics, Natural Hazards and Risk, vol 14 n° 1 (2023)
PermalinkAssessing spatio-temporal mapping and monitoring of climatic variability using SPEI and RF machine learning models / Saadia Sultan Wahlaa in Geocarto international, vol 37 n° 27 ([20/12/2022])
PermalinkAutomatic detection of suspected sewage discharge from coastal outfalls based on Sentinel-2 imagery / Yuxin Wang in Science of the total environment, vol 853 (December 2022)
PermalinkConsistency assessment of multi-date PlanetScope imagery for seagrass percent cover mapping in different seagrass meadows / Pramaditya Wicaksono in Geocarto international, vol 37 n° 27 ([20/12/2022])
PermalinkAbove ground biomass estimation from UAV high resolution RGB images and LiDAR data in a pine forest in Southern Italy / Mauro Maesano in iForest, biogeosciences and forestry, vol 15 n° 6 (December 2022)
PermalinkAutomatic registration method of multi-source point clouds based on building facades matching in urban scenes / Yumin Tan in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 12 (December 2022)
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