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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)
[article]
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 IGN] champ aléatoire conditionnel
[Termes IGN] classification
[Termes IGN] classification basée sur les régions
[Termes IGN] densité des points
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] Hong-Kong
[Termes IGN] modèle 3D de l'espace urbain
[Termes IGN] Paris (75)
[Termes IGN] scène urbaine
[Termes IGN] segmentation en régions
[Termes IGN] segmentation hiérarchique
[Termes IGN] segmentation sémantique
[Termes IGN] semis de pointsRésumé : (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 Affiliation des auteurs : non IGN 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
in ISPRS Journal of photogrammetry and remote sensing > vol 153 (July 2019) . - pp 151 - 165[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2019071 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019073 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019072 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt CNN-based dense image matching for aerial remote sensing images / Shunping Ji in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 6 (June 2019)
[article]
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 IGN] appariement d'images
[Termes IGN] appariement dense
[Termes IGN] apprentissage profond
[Termes IGN] Chine
[Termes IGN] couple stéréoscopique
[Termes IGN] image aérienne
[Termes IGN] Munich
[Termes IGN] réseau neuronal convolutif
[Termes IGN] Stuttgart
[Termes IGN] ville
[Termes IGN] zone urbaineRésumé : (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 Affiliation des auteurs : non IGN 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
in Photogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 6 (June 2019) . - pp 415 - 424[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2019061 SL Revue Centre de documentation Revues en salle Disponible 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)
[article]
Titre : 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 Type de document : Article/Communication Auteurs : Xin Huang, Auteur ; Ying Wang, Auteur Année de publication : 2019 Article en page(s) : pp 119 - 131 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] arbre urbain
[Termes IGN] Chine
[Termes IGN] ilot thermique urbain
[Termes IGN] image Landsat-8
[Termes IGN] image Landsat-TIRS
[Termes IGN] image ZiYuan-3
[Termes IGN] morphologie urbaine
[Termes IGN] régression multiple
[Termes IGN] température au sol
[Termes IGN] Wuhan (Chine)Résumé : (Auteur) The Urban heat island (UHI) effect is an increasingly serious problem in urban areas. Information on the driving forces of intra-urban temperature variation is crucial for ameliorating the urban thermal environment. Although prior studies have suggested that urban morphology (e.g., landscape pattern, land-use type) can significantly affect land surface temperature (LST), few studies have explored the comprehensive effect of 2D and 3D urban morphology on LST in different urban functional zones (UFZs), especially at a fine scale. Therefore, in this research, we investigated the relationship between 2D/3D urban morphology and summer daytime LST in Wuhan, a representative megacity in Central China, which is known for its extremely hot weather in summer, by adopting high-resolution remote sensing data and geographical information data. The “urban morphology” in this study consists of 2D urban morphological parameters, 3D urban morphological parameters, and UFZs. Our results show that: (1) The LST is significantly related to 2D and 3D urban morphological parameters, and the scattered distribution of buildings with high rise can facilitate the mitigation of LST. Although sky view factor (SVF) is an important measure of 3D urban geometry, its influence on LST is complicated and context-dependent. (2) Trees are the most influential factor in reducing LST, and the cooling efficiency mainly depends on their proportions. The fragmented and irregular distribution of grass/shrubs also plays a significant role in alleviating LST. (3) With respect to UFZs, the residential zone is the largest heat source, whereas the highest LST appears in commercial and industrial zones. (4) Results of the multivariate regression and variation partitioning indicate that the relative importance of 2D and 3D urban morphological parameters on LST varies among different UFZs and 2D morphology outperforms 3D morphology in LST modulation. The results are generally consistent in spring, summer and autumn. These findings can provide insights for urban planners and designers on how to mitigate the surface UHI (SUHI) effect via rational landscape design and urban management during summer daytime. Numéro de notice : A2019-456 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.04.010 Date de publication en ligne : 22/04/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.04.010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92869
in ISPRS Journal of photogrammetry and remote sensing > vol 152 (June 2019) . - pp 119 - 131[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2019061 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019063 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019062 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt 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)
[article]
Titre : A new stochastic simulation algorithm for image-based classification : Feature-space indicator simulation Type de document : Article/Communication Auteurs : Qing Wang, Auteur ; Hua Sun, Auteur ; Ruopu Li, Auteur ; Guangxing Wang, Auteur Année de publication : 2019 Article en page(s) : pp 145 - 165 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] forêt
[Termes IGN] géostatistique
[Termes IGN] image Landsat-OLI
[Termes IGN] image SPOT 5
[Termes IGN] Mongolie intérieure (Chine)
[Termes IGN] occupation du sol
[Termes IGN] précision de la classification
[Termes IGN] utilisation du sol
[Termes IGN] variogrammeRésumé : (Auteur) Traditional parametric methods for classification of land use and land cover (LULC) types using remote sensing imagery assume a global distribution model and fail to consider local variation of categorical variables. Differently, non-parametric methods do not make any statistical assumptions but are typically sensitive to the sample sizes of training sample data that usually require a high cost to collect in the field. Geostatistical classifiers, such as indicator kriging and simulation, are local variability-based methods that exhibit great potential for image-based classification of LULC types. However, variogram models required are highly sensitive to the spatial configuration of training samples as well as sample size given a study area. Moreover, when a large number of spectral variables are considered into kriging systems, modeling the variograms and cross-variograms would be problematic. To circumvent these issues, this study extended the geostatistical methods from a 2-dimensional geographic space to a m-dimensional image feature space to derive feature-space indicator variograms (FSIVs). Moreover, a novel stochastic simulation classification algorithm, Feature-Space Indicator Simulation (FSIS), was proposed and examined for classification of LULC types in Duolun County located in Inner Mongolia and in Huang-Feng-Qiao (HFQ) forest farm, Hunan of China. In Duolun, six LULC types were involved and in HFQ a complicated forest landscape consisting of nine forest types plus water, built-up area, and agricultural/bare soil, was classified. The classification results of FSIS were compared with another feature-space geostatistical classifier – feature-space indicator kriging (FSIK), a traditional parametric method – maximum likelihood (ML), a widely used nonparametric method – support vector machine (SVM), and a recently popular method – random forest (RF). The results showed that compared with ML, SVM and RF, in both study areas FSIS statistically significantly increased the accuracy of the classifications by 10.0–29.9% for percentage correct and 19.0–47.6% for Kappa statistic. Compared with FSIK, FSIS also improved the classification accuracy but the accuracy increases were relatively smaller with the percentages correct of 3.5% and 7.6% and the Kappa values of 4.6% and 8.6% for Duolun and HFQ, respectively. Moreover, FSIS led to the spatial uncertainties of the classification estimates as the quality measure of the estimates. In addition, the results also demonstrated that FSIVs were sensitive to the within-class heterogeneity but not very much to the size of training samples. Overall, FSIS exhibited the greater potential to improve the classification accuracy of LULC and forest types using remote sensing image. Numéro de notice : A2019-457 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.04.011 Date de publication en ligne : 25/04/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.04.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92871
in ISPRS Journal of photogrammetry and remote sensing > vol 152 (June 2019) . - pp 145 - 165[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2019061 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019063 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019062 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt 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)
[article]
Titre : Estimation of the forest stand mean height and aboveground biomass in Northeast China using SAR Sentinel-1B, multispectral Sentinel-2A, and DEM imagery Type de document : Article/Communication Auteurs : Yanan Liu, Auteur ; Weishu Gong, Auteur ; Yanqiu Xing, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 277 - 289 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] biomasse aérienne
[Termes IGN] biomasse forestière
[Termes IGN] Chine
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] hauteur des arbres
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] modèle numérique de surface
[Termes IGN] polarisationRésumé : (Auteur) Accurate mapping the forest stand mean height (FSMH) and aboveground biomass (AGB) with a high spatial resolution are important for monitoring carbon stocks on Earth and the variability and trends of terrestrial carbon fluxes. The recently launched Sentinel-1 (SAR) and Sentinel-2 (multispectral) missions offers a new opportunity to map FSMH and AGB. Here we present a methodological framework to map the FSMH and AGB at a resolution of 10 m in Yichun, Northeast China, by integrating field plots, Sentinel imagery, topographic data, and national geographical conditions monitoring data. First, a spatial continuous FSMH product was retrieved using an empirical model, which adopts the backscattering of SAR Sentinel-1B and the fraction of vegetation cover (FVC) variable from multispectral Sentinel-2A imagery. Subsequently, three AGB estimation models were developed for different forest types to link the field measurements to the FSMH, biophysical variables, spectral vegetation index, and topographic variables using the random forest algorithm. The mapping results show that the FSMH estimated using SAR backscatter values from VH polarization is more robust and accurate than that based on VV polarization. Furthermore, the three AGB estimation models based on three different forest types perform better than the model built by grouping all forest types together. The determination coefficient (R2) and root-mean-squared error (RMSE) range from 0.69 to 0.74 and 23.38 Mg/ha to 24.21 Mg/ha, respectively. Overall, our study demonstrates that the proposed methodological framework can be used to map the FSMH and AGB products at a high spatial resolution utilizing freely accessible Sentinel-1 SAR and Sentinel-2 multispectral imagery. Numéro de notice : A2019-211 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.03.016 Date de publication en ligne : 30/03/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.03.016 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92677
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