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Estimation and analysis of GPS inter-fequency clock biases from long-term triple-frequency observations / Fan Zhang in GPS solutions, vol 25 n° 4 (October 2021)  

Public [article]  inGPS solutions > vol 25 n° 4 (October 2021) . - n° 126 Titre : Estimation and analysis of GPS inter-fequency clock biases from long-term triple-frequency observations Type de document : Article/Communication Auteurs : Fan Zhang, Auteur ; Hongzhou Chai, Auteur ; Linyang Li, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 126 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] décalage d'horloge [Termes IGN] erreur systématique interfréquence d'horloge [Termes IGN] fréquence multiple [Termes IGN] phase GPS [Termes IGN] positionnement ponctuel précis [Termes IGN] station GNSS [Termes IGN] triple différence [Termes IGN] variation temporelle Résumé : (auteur) Usually, the difference between the satellite clocks computed with L1/L2 and clocks computed with L1/L5 is defined as inter-frequency clock bias (IFCB). It is critical to correct its L5 time-variant portion in the GNSS triple-frequency precise positioning. Using two years of observations from more than 100 stations worldwide, we use the epoch-differenced method to estimate IFCB for all available 12 GPS BLOCK-IIF satellites, and analyze its short-term and long-term variations. The experimental results indicate that the IFCB variations are clearly consistent for two satellites located in the same orbital plane, which perhaps means that the variations of IFCB are dependent on the orbital plane. We found that the IFCB of each Block-IIF satellite shows repetition characteristics over two years. The annual repetition cycle of 352 days of IFCB is consistent with the GPS year 351.4 days may originate from the rotation of satellites around the earth. GPS triple-frequency uncombined PPP is carried out using 9 globally distributed MGEX stations from June 1 to 30, 2018. The experimental results indicate that compared to the PPP solutions without IFCB corrections, GPS triple-frequency PPP can achieve an accuracy of 2.2, 3.8 and 11.4 mm in the north, east, and up components after correcting IFCB, which is an accuracy increase in 31.3%, 17.4%, and 13.0%, respectively. The average RMS of the phase posteriori residuals for each frequency is also reduced significantly, especially 79.1% for L5 frequency. Numéro de notice : A2021-565 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-021-01161-8 Date de publication en ligne : 10/07/2021 En ligne : https://doi.org/10.1007/s10291-021-01161-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98134 [article]

Unsupervised-restricted deconvolutional neural network for very high resolution remote-sensing image classification / Yiting Tao in IEEE Transactions on geoscience and remote sensing, vol 55 n° 12 (December 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 12 (December 2017) . - pp 6805 - 6823 Titre : Unsupervised-restricted deconvolutional neural network for very high resolution remote-sensing image classification Type de document : Article/Communication Auteurs : Yiting Tao, Auteur ; Miaozhong Xu, Auteur ; Fan Zhang, Auteur ; Bo Du, Auteur ; Liangpei Zhang, Auteur Année de publication : 2017 Article en page(s) : pp 6805 - 6823 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage non-dirigé [Termes IGN] classification pixellaire [Termes IGN] déconvolution [Termes IGN] image Geoeye [Termes IGN] image Quickbird [Termes IGN] méthode fondée sur le noyau [Termes IGN] réseau neuronal convolutif Résumé : (Auteur) As the acquisition of very high resolution (VHR) satellite images becomes easier owing to technological advancements, ever more stringent requirements are being imposed on automatic image interpretation. Moreover, per-pixel classification has become the focus of research interests in this regard. However, the efficient and effective processing and the interpretation of VHR satellite images remain a critical task. Convolutional neural networks (CNNs) have recently been applied to VHR satellite images with considerable success. However, the prevalent CNN models accept input data of fixed sizes and train the classifier using features extracted directly from the convolutional stages or the fully connected layers, which cannot yield pixel-to-pixel classifications. Moreover, training a CNN model requires large amounts of labeled reference data. These are challenging to obtain because per-pixel labeled VHR satellite images are not open access. In this paper, we propose a framework called the unsupervised-restricted deconvolutional neural network (URDNN). It can solve these problems by learning an end-to-end and pixel-to-pixel classification and handling a VHR classification using a fully convolutional network and a small number of labeled pixels. In URDNN, supervised learning is always under the restriction of unsupervised learning, which serves to constrain and aid supervised training in learning more generalized and abstract feature. To some degree, it will try to reduce the problems of overfitting and undertraining, which arise from the scarcity of labeled training data, and to gain better classification results using fewer training samples. It improves the generality of the classification model. We tested the proposed URDNN on images from the Geoeye and Quickbird sensors and obtained satisfactory results with the highest overall accuracy (OA) achieved as 0.977 and 0.989, respectively. Experiments showed that the combined effects of additional kernels and stages may have produced better results, and two-stage URDNN consistently produced a more stable result. We compared URDNN with four methods and found that with a small ratio of selected labeled data items, it yielded the highest and most stable results, whereas the accuracy values of the other methods quickly decreased. For some categories with fewer training pixels, accuracy for categories from other methods was considerably worse than that in URDNN, with the largest difference reaching almost 10%. Hence, the proposed URDNN can successfully handle the VHR image classification using a small number of labeled pixels. Furthermore, it is more effective than state-of-the-art methods. Numéro de notice : A2017-766 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2734697 En ligne : https://doi.org/10.1109/TGRS.2017.2734697 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88803 [article]

Registration of aerial imagery and lidar data in desert areas using sand ridges / Na Li in Photogrammetric record, vol 30 n° 151 (September - November 2015)  

Public [article]  inPhotogrammetric record > vol 30 n° 151 (September - November 2015) . - pp 263 – 278 Titre : Registration of aerial imagery and lidar data in desert areas using sand ridges Type de document : Article/Communication Auteurs : Na Li, Auteur ; Xianfeng Huang, Auteur ; Fan Zhang, Auteur ; Deren Li, Auteur Année de publication : 2015 Article en page(s) : pp 263 – 278 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] algorithme ICP [Termes IGN] crète (ligne) [Termes IGN] désert [Termes IGN] données lidar [Termes IGN] dune [Termes IGN] extraction de traits caractéristiques [Termes IGN] fusion de données multisource [Termes IGN] image aérienne [Termes IGN] recalage d'image [Termes IGN] semis de points Résumé : (Auteur) Image registration is a prerequisite for multisource data fusion. In this paper the problem of registering aerial images with lidar point clouds in desert areas is addressed. Compared with urban areas, registration in desert regions is difficult due to the lack of man-made features which are typically used in traditional methods. However, sand ridges can be used as registration primitives. Firstly, sand-ridge information is extracted from both the aerial image and the lidar point cloud. Secondly, by extending the iterative closest point (ICP) approach, a perspective-ICP algorithm is proposed that achieves data registration through matching sand ridges. To automatically deal with outliers, an adaptive weighting strategy is adopted. Experiments and assessment using data from Dunhuang, Gobi Desert, China, demonstrate that the method can achieve efficient and reliable registration for desert areas. Numéro de notice : A2015-562 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12110 Date de publication en ligne : 27/07/2015 En ligne : https://doi.org/10.1111/phor.12110 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77679 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
106-2015031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Registration of aerial imagery and lidar data in desert areas using the centroids of bushes as control information / Na Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 8 (August 2013)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 79 n° 8 (August 2013) . - pp 743 - 752 Titre : Registration of aerial imagery and lidar data in desert areas using the centroids of bushes as control information Type de document : Article/Communication Auteurs : Na Li, Auteur ; Xianfeng Huang, Auteur ; Fan Zhang, Auteur ; Le Wang, Auteur Année de publication : 2013 Article en page(s) : pp 743 - 752 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] appariement de données localisées [Termes IGN] brousse [Termes IGN] centroïde [Termes IGN] désert [Termes IGN] données lidar [Termes IGN] Gobi, désert du [Termes IGN] image aérienne [Termes IGN] Ransac (algorithme) [Termes IGN] semis de points Résumé : (Auteur) Geometric registration of multiple-source data is of great value for fusion processing and is very beneficial for the research of desert ecosystems. A lidar point cloud and optical image are two typical data that need to be integrated for data assimilation and information retrieval. This paper aims to solve the registration problem of aerial imagery and airborne lidar data in desert areas where traditional registration methods have difficulties in identifying registration primitives. In many deserts, such as the Sahara in Africa and Gobi in China, we observe that there are unevenly distributed desert bushes, which can be used as cues for registration. In this paper, we propose a registration approach using the centroids of bushes as registration primitives. This approach employs similar triangles created from both centroids as the evidence for matching and verifies the registration by the RANSAC algorithm. Experiments using data taken from the Dunhuang Gobi Desert in China show the registration surface model visually, and at the same time quantifies the deviation error, which corroborates that the proposed registration method is effective and feasible in desert areas. Numéro de notice : A2013-427 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.79.8.731 En ligne : https://doi.org/10.14358/PERS.79.8.731 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32565 [article]