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Graph-based block-level urban change detection using Sentinel-2 time series / Nan Wang in Remote sensing of environment, vol 274 (June 2022)  

Public [article]  inRemote sensing of environment > vol 274 (June 2022) . - n° 112993 Titre : Graph-based block-level urban change detection using Sentinel-2 time series Type de document : Article/Communication Auteurs : Nan Wang, Auteur ; Wei Li, Auteur ; Ran Tao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 112993 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse multivariée [Termes IGN] bâtiment [Termes IGN] Chine [Termes IGN] détection de changement [Termes IGN] espace vert [Termes IGN] extraction de traits caractéristiques [Termes IGN] graphe [Termes IGN] image Sentinel-MSI [Termes IGN] occupation du sol [Termes IGN] OpenStreetMap [Termes IGN] segmentation d'image [Termes IGN] série temporelle [Termes IGN] zone urbaine Résumé : (auteur) Remote sensing technology has been frequently used to obtain information on changes in urban land cover because of its vast spatial coverage and timeliness of observation. Block-level change detection with high temporal resolution image data provides fine detail of urban changes, is suitable for urban management, and has gradually received widespread attention. High-dimensional features are required to express the heterogeneous structure of the blocks. High-dimensional high-frequency time series, namely, multivariate time series, are formed by arranging high-dimensional features chronologically. Classic change detection methods treat multivariate time series as univariate time series one by one. Few studies have analyzed the change in a multivariate time series by considering all variables as an entirety. Therefore, a graph-based segmentation for multivariate time series algorithm (MTS-GS) is proposed in this paper. Specifically, 1) we construct a similarity matrix to explore the changing patterns of multivariate time series for seasonal change, trend change, abrupt change, and noise disturbance; 2) a multivariate time series graph is defined based on the changing patterns; and 3) the corresponding graph segmentation algorithm is proposed in the paper to detect the abrupt and trend changes under noise and seasonal disturbances. Sentinel-2 images of the rapidly developing third-tier city of Luoyang, Henan province, China, are adopted to validate the algorithm. The F1-score in the spatial domain is 84.1%; the producer's and the user's accuracy in the temporal dimension are 81.8% and 80.1%, respectively. Seven change types are defined and extracted, showing the development pattern and the efficiency of land use in the city. Furthermore, the proposed MTS-GS can be used for pixel-level change detection and performs well under various time intervals and cloud covers. Numéro de notice : A2022-399 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.112993 Date de publication en ligne : 16/03/2022 En ligne : https://doi.org/10.1016/j.rse.2022.112993 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100699 [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 IGN] Allemagne [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection d'objet [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] données multisources [Termes IGN] image aérienne [Termes IGN] modèle numérique de surface [Termes IGN] modèle stéréoscopique [Termes IGN] segmentation [Termes IGN] segmentation sémantique [Termes 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]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2020091	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2020093	DEP-RECP	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2020092	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

The impact of second-order ionospheric delays on the ZWD estimation with GPS and BDS measurements / Shaocheng Zhang in GPS solutions, vol 24 n° 2 (April 2020)  

Public [article]  inGPS solutions > vol 24 n° 2 (April 2020) Titre : The impact of second-order ionospheric delays on the ZWD estimation with GPS and BDS measurements Type de document : Article/Communication Auteurs : Shaocheng Zhang, Auteur ; Lei Fang, Auteur ; Guangxing Wang, Auteur ; Wei Li, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] champ géomagnétique [Termes IGN] décalage d'horloge [Termes IGN] données BeiDou [Termes IGN] données GPS [Termes IGN] gradient ionosphèrique [Termes IGN] méthode des moindres carrés [Termes IGN] positionnement ponctuel précis [Termes IGN] retard ionosphèrique [Termes IGN] retard troposphérique zénithal [Termes IGN] teneur verticale totale en électrons Résumé : (auteur) Since millimeter accuracy is required in many GNSS applications such as real-time zenith wet delay (ZWD) estimation, the higher-order ionospheric delays on GNSS signals are no longer negligible. We calculated the second-order ionospheric delays (I2) and analyzed the impact on the ZWD estimation with GPS-only and combined GPS/BDS observations. The undifferenced PPP model with fixed coordinates was used to estimate the ZWD and horizontal gradients. The method of blockwise sequential least squares was utilized to eliminate the receiver clock biases and compute the I2 impact on the ZWDs. The I2 delays on each GNSS satellite observations were calculated with the CODE final TEC map and the 12th generation of the international geomagnetic reference field (IGRF-12) model. The statistical results with the actual observation geometry show that the I2 delays can reach over 10 mm during the daytime, and the corresponding impact on the estimated ZWD can reach up to several millimeters. At station HKWS, the maximum I2 impact with GPS only reaches up to 3.1 mm and is still 2.4 mm when both GPS and BDS observations are used. The simulated I2 impact on the ZWD could reach several millimeters, even though the TEC and geomagnetic values were calculated from relatively moderate background models. Compared with the 5–10 mm precision of real-time ZWD estimation, the I2 delays must not be ignored, especially during high VTEC periods. Numéro de notice : A2020-082 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-020-0954-8 Date de publication en ligne : 04/02/2020 En ligne : https://doi.org/10.1007/s10291-020-0954-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94651 [article]

Calibration errors in determining slant Total Electron Content (TEC) from multi-GNSS data / Wei Li in Advances in space research, vol 63 n° 5 (1 March 2019)  

Public [article]  inAdvances in space research > vol 63 n° 5 (1 March 2019) . - pp 1670 - 1680 Titre : Calibration errors in determining slant Total Electron Content (TEC) from multi-GNSS data Type de document : Article/Communication Auteurs : Wei Li, Auteur ; Guangxing Wang, Auteur ; Jinzhong Mi, Auteur ; Shaocheng Zhang, Auteur Année de publication : 2019 Article en page(s) : pp 1670 - 1680 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] données BeiDou [Termes IGN] données Galileo [Termes IGN] données GNSS [Termes IGN] données GPS [Termes IGN] étalonnage des données [Termes IGN] ligne de base [Termes IGN] propagation ionosphérique [Termes IGN] simple différence [Termes IGN] teneur totale en électrons [Termes IGN] trajet multiple Résumé : (Auteur) The global navigation satellite system (GNSS) is presently a powerful tool for sensing the Earth's ionosphere. For this purpose, the ionospheric measurements (IMs), which are by definition slant total electron content biased by satellite and receiver differential code biases (DCBs), need to be first extracted from GNSS data and then used as inputs for further ionospheric representations such as tomography. By using the customary phase-to-code leveling procedure, this research comparatively evaluates the calibration errors on experimental IMs obtained from three GNSS, namely the US Global Positioning System (GPS), the Chinese BeiDou Navigation Satellite System (BDS), and the European Galileo. On the basis of ten days of dual-frequency, triple-GNSS observations collected from eight co-located ground receivers that independently form short-baselines and zero-baselines, the IMs are determined for each receiver for all tracked satellites and then for each satellite differenced for each baseline to evaluate their calibration errors. As first derived from the short-baseline analysis, the effects of calibration errors on IMs range, in total electron content units, from 1.58 to 2.16, 0.70 to 1.87, and 1.13 to 1.56 for GPS, Galileo, and BDS, respectively. Additionally, for short-baseline experiment, it is shown that the code multipath effect accounts for their main budget. Sidereal periodicity is found in single-differenced (SD) IMs for GPS and BDS geostationary satellites, and the correlation of SD IMs over two consecutive days achieves the maximum value when the time tag is around 4 min. Moreover, as byproducts of zero-baseline analysis, daily between-receiver DCBs for GPS are subject to more significant intra-day variations than those for BDS and Galileo. Numéro de notice : A2019-172 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.asr.2018.11.020 Date de publication en ligne : 05/12/2018 En ligne : https://doi.org/10.1016/j.asr.2018.11.020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92624 [article]

Multisource remote sensing data classification based on convolutional neural network / Xiaodong Xu in IEEE Transactions on geoscience and remote sensing, vol 56 n° 2 (February 2018)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 56 n° 2 (February 2018) . - pp 937 - 949 Titre : Multisource remote sensing data classification based on convolutional neural network Type de document : Article/Communication Auteurs : Xiaodong Xu, Auteur ; Wei Li, Auteur ; Qiong Ran, Auteur ; Qian Du, Auteur ; Lianru Gao, Auteur ; Bing Zhang, Auteur Année de publication : 2018 Article en page(s) : pp 937 - 949 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] classification par réseau neuronal [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] extraction automatique [Termes IGN] extraction de traits caractéristiques [Termes IGN] image hyperspectrale [Termes IGN] réseau neuronal convolutif Résumé : (Auteur) As a list of remotely sensed data sources is available, how to efficiently exploit useful information from multisource data for better Earth observation becomes an interesting but challenging problem. In this paper, the classification fusion of hyperspectral imagery (HSI) and data from other multiple sensors, such as light detection and ranging (LiDAR) data, is investigated with the state-of-the-art deep learning, named the two-branch convolution neural network (CNN). More specific, a two-tunnel CNN framework is first developed to extract spectral-spatial features from HSI; besides, the CNN with cascade block is designed for feature extraction from LiDAR or high-resolution visual image. In the feature fusion stage, the spatial and spectral features of HSI are first integrated in a dual-tunnel branch, and then combined with other data features extracted from a cascade network. Experimental results based on several multisource data demonstrate the proposed two-branch CNN that can achieve more excellent classification performance than some existing methods. Numéro de notice : A2018-191 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2756851 Date de publication en ligne : 16/10/2017 En ligne : https://doi.org/10.1109/TGRS.2017.2756851 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89856 [article]

Atmospheric correction over coastal waters using multilayer neural networks / Yongzhen Fan in Remote sensing of environment, vol 199 (15 September 2017)  

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Comparison of landslide susceptibility mapping based on statistical index, certainty factors, weights of evidence and evidential belief function models / Kai Cui in Geocarto international, vol 32 n° 9 (September 2017)  

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Sparse and low-rank graph for discriminant analysis of hyperspectral imagery / Wei Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)  

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Automated detection of Martian gullies from HiRISE imagery / Wei Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 12 (December 2015)  

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Local binary patterns and extreme learning machine for hyperspectral imagery classification / Wei Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 7 (July 2015)  

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Collaborative representation for hyperspectral anomaly detection / Wei Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)  

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Decision fusion in kernel-induced spaces for hyperspectral image classification / Wei Li in IEEE Transactions on geoscience and remote sensing, vol 52 n° 6 Tome 2 (June 2014)  

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