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Image classification-based ground filtering of point clouds extracted from UAV-based aerial photos / Volkan Yilmaz in Geocarto international, vol 33 n° 3 (March 2018)
[article]
Titre : Image classification-based ground filtering of point clouds extracted from UAV-based aerial photos Type de document : Article/Communication Auteurs : Volkan Yilmaz, Auteur ; Berkant Konakoglu, Auteur ; Cigdem Serifoglu, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 310 - 320 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme de filtrage
[Termes IGN] classification dirigée
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données localisées 3D
[Termes IGN] drone
[Termes IGN] image aérienne
[Termes IGN] modèle numérique de surface
[Termes IGN] semis de points
[Termes IGN] TurquieRésumé : (Auteur) With the advent of unmanned aerial vehicles (UAVs) for mapping applications, it is possible to generate 3D dense point clouds using stereo images. This technology, however, has some disadvantages when compared to Light Detection and Ranging (LiDAR) system. Unlike LiDAR, digital cameras mounted on UAVs are incapable of viewing beneath the canopy, which leads to sparse points on the bare earth surface. In such cases, it is more challenging to remove points belonging to above-ground objects using ground filtering algorithms generated especially for LiDAR data. To tackle this problem, a methodology employing supervised image classification for filtering 3D point clouds is proposed in this study. A classified image is overlapped with the point cloud to determine the ground points to be used for digital elevation model (DEM) generation. Quantitative evaluation results showed that filtering the point cloud with this methodology has a good potential for high-resolution DEM generation. Numéro de notice : A2018-035 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1250825 En ligne : https://doi.org/10.1080/10106049.2016.1250825 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89213
in Geocarto international > vol 33 n° 3 (March 2018) . - pp 310 - 320[article]Important LiDAR metrics for discriminating forest tree species in Central Europe / Yifang Shi in ISPRS Journal of photogrammetry and remote sensing, vol 137 (March 2018)
[article]
Titre : Important LiDAR metrics for discriminating forest tree species in Central Europe Type de document : Article/Communication Auteurs : Yifang Shi, Auteur ; Tiejun Wang, Auteur ; Andrew K. Skidmore, Auteur ; Marco Heurich, Auteur Année de publication : 2018 Article en page(s) : pp 163 - 174 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] Allemagne
[Termes IGN] arbre (flore)
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] Europe centrale
[Termes IGN] forêt tempérée
[Termes IGN] morphologie mathématiqueRésumé : (Auteur) Numerous airborne LiDAR-derived metrics have been proposed for classifying tree species. Yet an in-depth ecological and biological understanding of the significance of these metrics for tree species mapping remains largely unexplored. In this paper, we evaluated the performance of 37 frequently used LiDAR metrics derived under leaf-on and leaf-off conditions, respectively, for discriminating six different tree species in a natural forest in Germany. We firstly assessed the correlation between these metrics. Then we applied a Random Forest algorithm to classify the tree species and evaluated the importance of the LiDAR metrics. Finally, we identified the most important LiDAR metrics and tested their robustness and transferability. Our results indicated that about 60% of LiDAR metrics were highly correlated to each other (|r| > 0.7). There was no statistically significant difference in tree species mapping accuracy between the use of leaf-on and leaf-off LiDAR metrics. However, combining leaf-on and leaf-off LiDAR metrics significantly increased the overall accuracy from 58.2% (leaf-on) and 62.0% (leaf-off) to 66.5% as well as the kappa coefficient from 0.47 (leaf-on) and 0.51 (leaf-off) to 0.58. Radiometric features, especially intensity related metrics, provided more consistent and significant contributions than geometric features for tree species discrimination. Specifically, the mean intensity of first-or-single returns as well as the mean value of echo width were identified as the most robust LiDAR metrics for tree species discrimination. These results indicate that metrics derived from airborne LiDAR data, especially radiometric metrics, can aid in discriminating tree species in a mixed temperate forest, and represent candidate metrics for tree species classification and monitoring in Central Europe. Numéro de notice : A2018-080 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.02.002 Date de publication en ligne : 07/02/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.02.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89442
in ISPRS Journal of photogrammetry and remote sensing > vol 137 (March 2018) . - pp 163 - 174[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2018031 RAB Revue Centre de documentation En réserve L003 Disponible 081-2018033 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2018032 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Large off-nadir scan angle of airborne LiDAR can severely affect the estimates of forest structure metrics / Jing Liu in ISPRS Journal of photogrammetry and remote sensing, vol 136 (February 2018)
[article]
Titre : Large off-nadir scan angle of airborne LiDAR can severely affect the estimates of forest structure metrics Type de document : Article/Communication Auteurs : Jing Liu, Auteur ; Andrew K. Skidmore, Auteur ; Simon D. Jones, Auteur ; Tiejun Wang, Auteur ; Marco Heurich, Auteur ; Xi Zhu, Auteur ; Yifang Shi, Auteur Année de publication : 2018 Article en page(s) : pp 13 - 25 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] angle de visée
[Termes IGN] Bavière (Allemagne)
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] instrument aéroporté
[Termes IGN] parc naturel régional
[Termes IGN] placette d'échantillonnage
[Termes IGN] structure d'un peuplement forestierRésumé : (Auteur) Gap fraction (Pgap) and vertical gap fraction profile (vertical Pgap profile) are important forest structural metrics. Accurate estimation of Pgap and vertical Pgap profile is therefore critical for many ecological applications, including leaf area index (LAI) mapping, LAI profile estimation and wildlife habitat modelling. Although many studies estimated Pgap and vertical Pgap profile from airborne LiDAR data, the scan angle was often overlooked and a nadir view assumed. However, the scan angle can be off-nadir and highly variable in the same flight strip or across different flight strips. In this research, the impact of off-nadir scan angle on Pgap and vertical Pgap profile was evaluated, for several forest types. Airborne LiDAR data from nadir (0°∼7°), small off-nadir (7°∼23°), and large off-nadir (23°∼38°) directions were used to calculate both Pgap and vertical Pgap profile. Digital hemispherical photographs (DHP) acquired during fieldwork were used as references for validation. Our results show that angular Pgap from airborne LiDAR correlates well with angular Pgap from DHP (R2 = 0.74, 0.87, and 0.67 for nadir, small off-nadir and large off-nadir direction). But underestimation of Pgap from LiDAR amplifies at large off-nadir scan angle. By comparing Pgap and vertical Pgap profiles retrieved from different directions, it is shown that scan angle impact on Pgap and vertical Pgap profile differs amongst different forest types. The difference is likely to be caused by different leaf angle distribution and canopy architecture in these forest types. Statistical results demonstrate that the scan angle impact is more severe for plots with discontinuous or sparse canopies. These include coniferous plots, and deciduous or mixed plots with between-crown gaps. In these discontinuous plots, Pgap and vertical Pgap profiles are maximum when observed from nadir direction, and then rapidly decrease with increasing scan angle. The results of this research have many important practical implications. First, it is suggested that large off-nadir scan angle of airborne LiDAR should be avoided to ensure a more accurate Pgap and LAI estimation. Second, the angular dependence of vertical Pgap profiles observed from airborne LiDAR should be accounted for, in order to improve the retrieval of LAI profiles, and other quantitative canopy structural metrics. This is especially necessary when using multi-temporal datasets in discontinuous forest types. Third, the anisotropy of Pgap and vertical Pgap profile observed by airborne LiDAR, can potentially help to resolve the anisotropic behavior of canopy reflectance, and refine the inversion of biophysical and biochemical properties from passive multispectral or hyperspectral data Numéro de notice : A2018-072 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.12.004 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.12.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89432
in ISPRS Journal of photogrammetry and remote sensing > vol 136 (February 2018) . - pp 13 - 25[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2018021 RAB Revue Centre de documentation En réserve L003 Disponible 081-2018023 RAB Revue Centre de documentation En réserve L003 Disponible 081-2018022 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Littoral, "Ricochet" ausculte / Marielle Mayo in Géomètre, n° 2155 (février 2018)
[article]
Titre : Littoral, "Ricochet" ausculte Type de document : Article/Communication Auteurs : Marielle Mayo, Auteur Année de publication : 2018 Article en page(s) : pp 48 - 51 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] acquisition de données
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] drone
[Termes IGN] éboulement
[Termes IGN] érosion côtière
[Termes IGN] falaise
[Termes IGN] image infrarouge
[Termes IGN] image panchromatique
[Termes IGN] modélisation 3D
[Termes IGN] Normandie (région 2016)
[Termes IGN] récepteur GPS
[Termes IGN] restitution
[Termes IGN] trait de côteRésumé : (Auteur) En Normandie, des chercheurs et des élus travaillent main dans la main pour améliorer la connaissance des risques littoraux et intégrer les changements côtiers dans les projets d'aménagement. Numéro de notice : A2018-028 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89189
in Géomètre > n° 2155 (février 2018) . - pp 48 - 51[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 063-2018021 RAB Revue Centre de documentation En réserve L003 Disponible 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)
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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 convolutifRésumé : (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
in IEEE Transactions on geoscience and remote sensing > vol 56 n° 2 (February 2018) . - pp 937 - 949[article]Predicting temperate forest stand types using only structural profiles from discrete return airborne lidar / Melissa Fedrigo in ISPRS Journal of photogrammetry and remote sensing, vol 136 (February 2018)PermalinkRobust interpolation of DEMs from lidar-derived elevation data / Chuanfa Chen in IEEE Transactions on geoscience and remote sensing, vol 56 n° 2 (February 2018)PermalinkValue of airborne laser scanning and digital aerial photogrammetry data in forest decision making / Annika S. Kangas in Silva fennica, vol 52 n° 1 ([01/02/2018])PermalinkAirborne laser scanning for tree diameter distribution modelling: a comparison of different modelling alternatives in a tropical single-species plantation / Matti Maltamo in Forestry, an international journal of forest research, vol 91 n° 1 (January 2018)PermalinkAssessing forest windthrow damage using single-date, post-event airborne laser scanning data / Gherardo Chirici in Forestry, an international journal of forest research, vol 91 n° 1 (January 2018)PermalinkAutomated extraction of hydrographically corrected contours for the conterminous United States: the US Geological Survey US Topo product / Samantha T. Arundel in Cartography and Geographic Information Science, Vol 45 n° 1 (January 2018)PermalinkColorisation of LiDAR point cloud / Mathieu Brédif (2018)PermalinkPermalinkPermalinkPermalink