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Combining spatiotemporal fusion and object-based image analysis for improving wetland mapping in complex and heterogeneous urban landscapes / Meng Zhang in Geocarto international, vol 34 n° 10 (October 2019)  

Public [article]  inGeocarto international > vol 34 n° 10 (October 2019) . - pp 1144 - 1161 Titre : Combining spatiotemporal fusion and object-based image analysis for improving wetland mapping in complex and heterogeneous urban landscapes Type de document : Article/Communication Auteurs : Meng Zhang, Auteur ; Yongnian Zeng, Auteur ; Wei Huang, Auteur ; Songnian Li, Auteur Année de publication : 2019 Article en page(s) : pp 1144 - 1161 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse d'image orientée objet [Termes descripteurs IGN] Chine [Termes descripteurs IGN] données spatiotemporelles [Termes descripteurs IGN] fusion d'images [Termes descripteurs IGN] hétérogénéité [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image Landsat-OLI [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] paysage urbain [Termes descripteurs IGN] zone humide Résumé : (auteur) Remote sensing has been proven promising in wetland mapping. However, conventional methods in a complex and heterogeneous urban landscape usually use mono temporal Landsat TM/ETM + images, which have great uncertainty due to the spectral similarity of different land covers, and pixel-based classifications may not meet the accuracy requirement. This paper proposes an approach that combines spatiotemporal fusion and object-based image analysis, using the spatial and temporal adaptive reflectance fusion model to generate a time series of Landsat 8 OLI images on critical dates of sedge swamp and paddy rice, and the time series of MODIS NDVI to calculate phenological parameters for identifying wetlands with an object-based method. The results of a case study indicate that different types of wetlands can be successfully identified, with 92.38%. The overall accuracy and 0.85 Kappa coefficient, and 85% and 90% for the user’s accuracies of sedge swamp and paddy respectively. Numéro de notice : A2019-302 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1474275 date de publication en ligne : 17/05/2018 En ligne : https://doi.org/10.1080/10106049.2018.1474275 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93222 [article]

Analysis of higher-order ionospheric effects on GNSS precise point positioning in the China area / Yaozong Zhou in Survey review, vol 51 n° 368 (September 2019)  

Public [article]  inSurvey review > vol 51 n° 368 (September 2019) . - pp 442 - 449 Titre : Analysis of higher-order ionospheric effects on GNSS precise point positioning in the China area Type de document : Article/Communication Auteurs : Yaozong Zhou, Auteur ; Cuilin Kuang, Auteur ; Changsheng Cai, Auteur Année de publication : 2019 Article en page(s) : pp 442 - 449 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes descripteurs IGN] Bernese [Termes descripteurs IGN] Chine [Termes descripteurs IGN] correction ionosphérique [Termes descripteurs IGN] erreur de positionnement [Termes descripteurs IGN] perturbation ionosphérique [Termes descripteurs IGN] positionnement par GNSS [Termes descripteurs IGN] positionnement ponctuel précis Résumé : (Auteur) Higher-order ionospheric error may reach several millimetres in the case of high ionospheric activity, which affects some high-accuracy applications, such as crustal movement monitoring, earthquake disaster prediction, plate motion monitoring and coordinate frame maintenance. However, accounting for higher-order ionospheric error is not yet a common strategy for regional global navigation satellite system (GNSS) network data processing. This study investigates the higher-order ionospheric effects on precise point positioning (PPP) in the China area with GNSS data selected from some IGS stations and some CMONOC stations. The Bernese GNSS software was used to calculate the effects of higher-order ionospheric error on the PPP estimated coordinates. The numerical results show that higher-order ionospheric positioning errors become larger as the station latitude decreases or the ionospheric activity increases. In addition, the positioning errors in the north direction are larger than those in the east and up directions and may reach 6 mm in south China. Numéro de notice : A2019-369 Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2018.1478483 date de publication en ligne : 24/12/2018 En ligne : https://doi.org/10.1080/00396265.2018.1478483 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93475 [article]

High-resolution large-area digital orthophoto map generation using LROC NAC images / Kaichang Di in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 7 (July 2019)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 7 (July 2019) . - pp 481 - 491 Titre : High-resolution large-area digital orthophoto map generation using LROC NAC images Type de document : Article/Communication Auteurs : Kaichang Di, Auteur ; Jia Mengna, Auteur ; Xin Xin, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 481 - 491 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] Chine [Termes descripteurs IGN] compensation par bloc [Termes descripteurs IGN] erreur de positionnement [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] Lune [Termes descripteurs IGN] modèle géométrique de prise de vue [Termes descripteurs IGN] modèle numérique de terrain [Termes descripteurs IGN] orthoimage [Termes descripteurs IGN] orthophotoplan numérique [Termes descripteurs IGN] zone homogène Résumé : (auteur) The Chang'e-5 mission of China is planned to be launched in 2019 to the landing area near Mons Rümker located in Oceanus Procellarum. Aiming to generate a high-resolution and high-quality digital orthophoto map (DOM) of the planned landing area for supporting the mission and various scientific analyses, this study developed a systematic and effective method for large-area seamless DOM production. The mapping results of the Chang'e-5 landing area using over 700 Lunar Reconnaissance Orbiter Camera (LROC) Narrow Angle Camera (NAC) images are presented. The resultant seamless DOM has a resolution of 1.5 m, covers a large area of 20° in longitude and 4° in latitude, and is tied to SLDEM2015. The results demonstrate that the proposed method can reduce the geometric inconsistencies among the LROC NAC images to the subpixel level and the positional errors with respect to the reference digital elevation model to about one grid cell size. Numéro de notice : A2019-257 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.7.481 date de publication en ligne : 01/07/2019 En ligne : https://doi.org/10.14358/PERS.85.7.481 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93052 [article]

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Structural segmentation and classification of mobile laser scanning point clouds with large variations in point density / Yuan Li in ISPRS Journal of photogrammetry and remote sensing, vol 153 (July 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 153 (July 2019) . - pp 151 - 165 Titre : Structural segmentation and classification of mobile laser scanning point clouds with large variations in point density Type de document : Article/Communication Auteurs : Yuan Li, Auteur ; Bo Wu, Auteur ; Xuming Ge, Auteur Année de publication : 2019 Article en page(s) : pp 151 - 165 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] classification [Termes descripteurs IGN] densité des points [Termes descripteurs IGN] espace urbain [Termes descripteurs IGN] Hong-Kong [Termes descripteurs IGN] Paris (75) [Termes descripteurs IGN] scène urbaine [Termes descripteurs IGN] segmentation hiérarchique [Termes descripteurs IGN] segmentation sémantique [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] télémétrie laser mobile Résumé : (Auteur) Objects are formed by various structures and such structural information is essential for the identification of objects, especially for street facilities presented by mobile laser scanning (MLS) data with abundant details. However, due to the large volume of data, large variations in point density, noise and complexity of scanned scenes, the achievement of effective decomposition of objects into physical meaningful structures remains a challenge issue. And structural information has been rarely considered to improve the accuracy of distinguishing between objects with global or local similarity, such as traffic signs and traffic lights. Therefore, we propose a structural segmentation and classification method for MLS point clouds that is efficient and robust to variations in point density and complex urban scenes. During the segmentation stage, a novel region growing approach and a multi-size supervoxel segmentation algorithm robust to noise and varying density are combined to extract effective local shape descriptors. Structural components with physically meaningful labels are generated via structural labelling and clustering. During the classification stage, we consider the structural information at various scales and locations and encode it into a conditional random-field model for unary and pairwise inferences. High-order potentials are also introduced into the conditional random field to eliminate regional label noise. These high-order potentials are defined upon regions independent of connection relationships and can therefore take effect on isolated nodes. Experiments with two MLS datasets of typical urban scenes in Paris and Hong Kong were used to evaluate the performance of the proposed method. Nine and eleven different object classes were recognized from these two datasets with overall accuracies of 97.13% and 95.79%, respectively, indicating the effectiveness of the proposed method of interpreting complex urban scenes from point clouds with large variations in point density. Compared with previous studies on the Paris dataset, our method was able to recognize more classes and obtained a mean F1-score of 72.70% of seven common classes, being higher than the best of previous results. Numéro de notice : A2019-262 Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.05.007 date de publication en ligne : 28/05/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.05.007 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93075 [article]

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CNN-based dense image matching for aerial remote sensing images / Shunping Ji in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 6 (June 2019)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 6 (June 2019) . - pp 415 - 424 Titre : CNN-based dense image matching for aerial remote sensing images Type de document : Article/Communication Auteurs : Shunping Ji, Auteur ; Jin Liu, Auteur ; Meng Lu, Auteur Année de publication : 2019 Article en page(s) : pp 415 - 424 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] appariement d'images [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] Chine [Termes descripteurs IGN] couple stéréoscopique [Termes descripteurs IGN] image aérienne [Termes descripteurs IGN] Munich [Termes descripteurs IGN] réseau neuronal convolutif [Termes descripteurs IGN] Stuttgart [Termes descripteurs IGN] ville [Termes descripteurs IGN] zone urbaine Résumé : (Auteur) Dense stereo matching plays a key role in 3D reconstruction. The capability of using deep learning in the stereo matching of remote sensing data is currently uncertain. This article investigated the application of deep learning–based stereo methods in aerial image series and proposed a deep learning–based multi-view dense matching framework. First, we applied three typical convolutional neural network models, MC-CNN, GC-Net, and DispNet, to aerial stereo pairs and compared the results with those of the SGM and a commercial software, SURE. Second, on different data sets, the generalization ability of each network is evaluated by using direct transfer learning with models pretrained on other data sets and by fine-tuning with a small number of target training data. Third, we present a deep learning–based multi-view dense matching framework where the multi-view geometry is introduced to further refine matching results. Three sets of aerial images as the main data sets and two open-source sets of street images as auxiliary data sets are used for testing. Experiments show that, first, the performance of deep learning–based stereo methods is slightly better than traditional methods. Second, both the GC-Net and the MC-CNN have demonstrated good generalization ability and can obtain satisfactory results on aerial images using a pretrained model on several available stereo benchmarks. Third, multi-view geometry constraints can further improve the performance of deep learning–based methods, which is better than that of the multi-view–based SGM and SURE. Numéro de notice : A2019-246 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.6.415 date de publication en ligne : 01/06/2019 En ligne : https://doi.org/10.14358/PERS.85.6.415 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93002 [article]

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Investigating the effects of 3D urban morphology on the surface urban heat island effect in urban functional zones by using high-resolution remote sensing data : A case study of Wuhan, Central China / Xin Huang in ISPRS Journal of photogrammetry and remote sensing, vol 152 (June 2019)  

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A new stochastic simulation algorithm for image-based classification : Feature-space indicator simulation / Qing Wang in ISPRS Journal of photogrammetry and remote sensing, vol 152 (June 2019)  

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Estimation of the forest stand mean height and aboveground biomass in Northeast China using SAR Sentinel-1B, multispectral Sentinel-2A, and DEM imagery / Yanan Liu in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)  

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A model for phased evacuations for disasters with spatio-temporal randomness / Menghui Li in International journal of geographical information science IJGIS, Vol 33 n° 5-6 (May - June 2019)  

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Multilane roads extracted from the OpenStreetMap urban road network using random forests / Yongyang Xu in Transactions in GIS, vol 23 n° 2 (April 2019)  

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Analysis of ocean tide loading displacements by GPS kinematic precise point positioning: a case study at the China coastal site SHAO / H. Zhao in Survey review, vol 51 n° 365 (March 2019)  

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Large-scale patterns in forest growth rates are mainly driven by climatic variables and stand characteristics / Hao Zhang in Forest ecology and management, vol 435 (1 March 2019)  

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A methodology with a distributed algorithm for large-scale trajectory distribution prediction / QiuLei Guo in International journal of geographical information science IJGIS, Vol 33 n° 3-4 (March - April 2019)  

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Real-time capturing of seismic waveforms using high-rate BDS, GPS and GLONASS observations: the 2017 Mw 6.5 Jiuzhaigou earthquake in China / Xingxing Li in GPS solutions, vol 23 n° 1 (January 2019)  

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Estimating forest structural attributes using UAV-LiDAR data in Ginkgo plantations / Kun Liu in ISPRS Journal of photogrammetry and remote sensing, vol 146 (December 2018)  

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