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Saline-soil deformation extraction based on an improved time-series InSAR approach / Wei Xiang in ISPRS International journal of geo-information, vol 10 n° 3 (March 2021)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 3 (March 2021) . - n° 112 Titre : Saline-soil deformation extraction based on an improved time-series InSAR approach Type de document : Article/Communication Auteurs : Wei Xiang, Auteur ; Rui Zhang, Auteur ; Guoxiang Liu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 112 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] bande C [Termes IGN] Chine [Termes IGN] déformation de surface [Termes IGN] extraction de traits caractéristiques [Termes IGN] image radar moirée [Termes IGN] image Sentinel-SAR [Termes IGN] série temporelle [Termes IGN] sol salin [Termes IGN] surface du sol [Termes IGN] variation saisonnière Résumé : (auteur) Significant seasonal fluctuations could occur in the regional scattering characteristics and surface deformation of saline soil, and cause decorrelation, which limits the application of the conventional time-series InSAR (TS-InSAR). For extending the saline-soil deformation monitoring capability, this paper presents an improved TS-InSAR approach, based on the interferometric coherence statistics and high-coherence interferogram refinement. By constructing a network of the refined interferograms, high-accuracy ground deformation can be extracted through the weighted least square estimation and the coherent target refinement. To extract the high-accuracy deformation of a representative saline soil area in the Qarhan Salt Lake, 119 C-band Sentinel-1A images collected between May 2015 and May 2020 are selected as the data source. Subsequently, 845 refined interferograms are selected from all possible interferograms to conduct the network inversion, based on the related thresholds (the temporal baseline 0.5, respectively). Compared with the conventional TS-InSAR measurements, both the accuracy and reliability of the extracted deformation results of the saline soil increased dramatically. Furthermore, the testing results indicate that the improved TS-InSAR method has advantages on the deformation extraction in the saline soil region, and is adaptive to reflecting the typical seasonal variations of the saline soil. Numéro de notice : A2021-234 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10030112 Date de publication en ligne : 27/02/2021 En ligne : https://doi.org/10.3390/ijgi10030112 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97230 [article]

Fusion of images and point clouds for the semantic segmentation of large-scale 3D scenes based on deep learning / Rui Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 143 (September 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 143 (September 2018) . - pp 85 - 96 Titre : Fusion of images and point clouds for the semantic segmentation of large-scale 3D scenes based on deep learning Type de document : Article/Communication Auteurs : Rui Zhang, Auteur ; Guangyun Li, Auteur ; Minglei Li, Auteur ; Li Wang, Auteur Année de publication : 2018 Article en page(s) : pp 85 - 96 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] apprentissage profond [Termes IGN] détection du bâti [Termes IGN] fusion de données [Termes IGN] réseau neuronal convolutif [Termes IGN] scène 3D [Termes IGN] segmentation sémantique [Termes IGN] semis de points Résumé : (Auteur) We address the issue of the semantic segmentation of large-scale 3D scenes by fusing 2D images and 3D point clouds. First, a Deeplab-Vgg16 based Large-Scale and High-Resolution model (DVLSHR) based on deep Visual Geometry Group (VGG16) is successfully created and fine-tuned by training seven deep convolutional neural networks with four benchmark datasets. On the val set in CityScapes, DVLSHR achieves a 74.98% mean Pixel Accuracy (mPA) and a 64.17% mean Intersection over Union (mIoU), and can be adapted to segment the captured images (image resolution 2832 ∗ 4256 pixels). Second, the preliminary segmentation results with 2D images are mapped to 3D point clouds according to the coordinate relationships between the images and the point clouds. Third, based on the mapping results, fine features of buildings are further extracted directly from the 3D point clouds. Our experiments show that the proposed fusion method can segment local and global features efficiently and effectively. Numéro de notice : A2018-356 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.04.022 Date de publication en ligne : 11/05/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.04.022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90590 [article]

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The study of key issues about integration of GNSS and strong-motion records for real-time earthquake monitoring / Rui Tu in Advances in space research, vol 58 n° 3 (August 2016)  

Public [article]  inAdvances in space research > vol 58 n° 3 (August 2016) . - pp 304 - 309 Titre : The study of key issues about integration of GNSS and strong-motion records for real-time earthquake monitoring Type de document : Article/Communication Auteurs : Rui Tu, Auteur ; Pengfei Zhang, Auteur ; Rui Zhang, Auteur ; Jinhai Liu, Auteur Année de publication : 2016 Article en page(s) : pp 304 - 309 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] coordonnées GPS [Termes IGN] erreur systématique [Termes IGN] intégration de données [Termes IGN] qualité des données [Termes IGN] surveillance géologique [Termes IGN] temps réel Résumé : (auteur) This paper has studied the key issues about integration of GNSS and strong-motion records for real-time earthquake monitoring. The validations show that the consistence of the coordinate system must be considered firstly to exclude the system bias between GNSS and strong-motion. The GNSS sampling rate is suggested about 1–5 Hz, and we should give the strong-motion’s baseline shift with a larger dynamic noise as its variation is very swift. The initialization time of solving the baseline shift is less than one minute, and ambiguity resolution strategy is not greatly improved the solution. The data quality is very important for the solution, we advised to use multi-frequency and multi-system observations. These ideas give an important guide for real-time earthquake monitoring and early warning by the tight integration of GNSS and strong-motion records. Numéro de notice : A2016-589 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.asr.2016.04.033 En ligne : http://dx.doi.org/10.1016/j.asr.2016.04.033 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81744 [article]