Centre dedocumentation
scientifique

Leaf and wood separation for individual trees using the intensity and density data of terrestrial laser scanners / Kai Tan in IEEE Transactions on geoscience and remote sensing, vol 59 n° 8 (August 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 8 (August 2021) . - pp 7038 - 7050 Titre : Leaf and wood separation for individual trees using the intensity and density data of terrestrial laser scanners Type de document : Article/Communication Auteurs : Kai Tan, Auteur ; Weiguo Zhang, Auteur ; Zhen Dong, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 7038 - 7050 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse de groupement [Termes IGN] bois [Termes IGN] densité du feuillage [Termes IGN] données lidar [Termes IGN] données TLS (télémétrie) [Termes IGN] feuille (végétation) [Termes IGN] réflectance spectrale [Termes IGN] semis de points Résumé : (auteur) Terrestrial laser scanning (TLS) is a highly effective and noninvasive technology for retrieving the structural and biophysical attributes of trees using 3-D high-accuracy and high-density point clouds. The separation of leaf and wood points in TLS data is a prerequisite for the accurate and reliable derivation of these attributes. In this study, a new method is proposed to separate the leaf and wood points of individual trees by combining the TLS radiometric (intensity) and geometric (density) data. The leaf points are separated from the wood ones through three steps. First, the corrected intensity data are used to separate a part of the leaf points preliminarily given the differences in reflectance characteristics. Second, the density data are adopted for the further separation of another part of the leaf points because the density of the remaining leaf points is smaller than that of the wood points. Finally, a connectivity clustering algorithm is conducted to form several clusters with different sizes (points) and the remaining leaf points are separated in accordance with the cluster sizes. Eight different trees are selected to evaluate the performance of the proposed method. The averaged overall accuracy and kappa coefficient of the eight trees are approximately 95% and 0.81, respectively. The results suggest that the combination of TLS intensity and density data can perform a superior separation of leaf and wood points in terms of efficiency and accuracy, and the proposed separation method can be accurately and robustly used for various trees with different species, sizes, and structures. Numéro de notice : A2021-633 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3032167 date de publication en ligne : 30/10/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3032167 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98295 [article]

Background tropospheric delay in geosynchronous synthetic aperture radar / Dexin Li in Remote sensing, vol 12 n° 18 (September-2 2020)  

Public [article]  inRemote sensing > vol 12 n° 18 (September-2 2020) . - 21 p. Titre : Background tropospheric delay in geosynchronous synthetic aperture radar Type de document : Article/Communication Auteurs : Dexin Li, Auteur ; Xiaoxiang Zhu, Auteur ; Zhen Dong, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 21 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] compensation [Termes IGN] décorrélation [Termes IGN] données météorologiques [Termes IGN] image à haute résolution [Termes IGN] image radar moirée [Termes IGN] modèle géométrique de prise de vue [Termes IGN] propagation troposphérique [Termes IGN] radar bistatique [Termes IGN] retard troposphérique [Termes IGN] synchronisation Résumé : (auteur) Spaceborne synthetic aperture radar (SAR) has been treated as a weather independent system for a long time. However, with the development of advanced SAR configurations, e.g., high resolution, bistatic, geosynchronous (GEO), the influence of tropospheric propagation error, which strongly depends on the weather, has begun to receive attention. In this paper, we focus on the effect of deterministic background tropospheric delay (BTD) during the image formation of GEO SAR. First, the decorrelation problems caused by the spatial variation and BTD are presented. Second, by combining with the SAR imaging geometry, the BTD error is decomposed as constant error, spatially variant error, and time variant error, the influences of which are analyzed under different circumstances. Third, an imaging method starting from the meteorological parameters and the GEO SAR systematic parameters is proposed to deal with the decorrelation problems. Finally, simulations with the dot-matrix targets are performed to validate the imaging method. Numéro de notice : A2020-632 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs12183081 date de publication en ligne : 20/09/2020 En ligne : https://doi.org/10.3390/rs12183081 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96053 [article]

Automated fusion of forest airborne and terrestrial point clouds through canopy density analysis / Wenxia Dai in ISPRS Journal of photogrammetry and remote sensing, vol 156 (October 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 156 (October 2019) . - pp 94 - 107 Titre : Automated fusion of forest airborne and terrestrial point clouds through canopy density analysis Type de document : Article/Communication Auteurs : Wenxia Dai, Auteur ; Bisheng Yang, Auteur ; Xinlian Liang, Auteur ; Zhen Dong, Auteur ; Ronggang Huang, Auteur ; Yunsheng Wang, Auteur ; Wuyan Li, Auteur Année de publication : 2019 Article en page(s) : pp 94 - 107 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] algorithme ICP [Termes IGN] canopée [Termes IGN] données TLS (télémétrie) [Termes IGN] Finlande [Termes IGN] forêt boréale [Termes IGN] fusion de données multisource [Termes IGN] image ADAR [Termes IGN] semis de points [Termes IGN] surveillance forestière Résumé : (Auteur) Airborne laser scanning (ALS) and terrestrial laser scanning (TLS) systems are effective ways to capture the 3D information of forests from complementary perspectives. Registration of the two sources of point clouds is necessary for various forestry applications. Since the forest point clouds show irregular and natural point distributions, standard registration methods working on geometric keypoints (e.g., points, lines, and planes) are likely to fail. Hence, we propose a novel method to register the ALS and TLS forest point clouds through density analysis of the crowns. The proposed method extracts mode-based keypoints by the mean shift method and aligns them by maximum likelihood estimation. Firstly, the differences in the point densities of the ALS and TLS crowns are minimized to produce analogous modes, which represent the local maxima of the underlying probability density function (PDF). The mode-based keypoints are then aligned through the coherent point drift (CPD) algorithm, which is independent of the descriptor similarities and considers the alignment as a maximum likelihood estimation problem. The sets of keypoints derived from the two data sources need not be equal. Finally, the recovered transformation is applied to the original point clouds and refined through the standard iterative closest point (ICP) algorithm. In contrast to some of the existing methods, the proposed method avoids the geometric description of the forest point clouds. Furthermore, additional information such as tree diameter or height is not required to evaluate the similarities. The experiments in this study were conducted in a Scandinavian boreal forest, located in Evo, Finland. The proposed method was tested on four datasets (ALS data: a circle with a diameter of 60 m, multi-scan TLS data: 32 × 32 m) with heterogeneous tree species and structures. The results showed that the proposed probabilistic-based method obtains a good performance with a 3D distance residual of 0.069 m, and improved the accuracy of the registration when compared with the existing methods. Numéro de notice : A2019-318 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : doi.org/10.1016/j.isprsjprs.2019.08.008 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.08.008 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93356 [article]

Réservation

Réserver ce document

Exemplaires (3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2019101	RAB	Revue	Centre de documentation	En réserve 3L	Disponible
081-2019103	DEP-RECP	Revue	LaSTIG	Dépôt en unité	Exclu du prêt
081-2019102	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

A new method for 3D individual tree extraction using multispectral airborne LiDAR point clouds / Wenxia Dai in ISPRS Journal of photogrammetry and remote sensing, vol 144 (October 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 144 (October 2018) . - pp 400 - 411 Titre : A new method for 3D individual tree extraction using multispectral airborne LiDAR point clouds Type de document : Article/Communication Auteurs : Wenxia Dai, Auteur ; Yang Bisheng, Auteur ; Zhen Dong, Auteur ; Ahmed Shaker, Auteur Année de publication : 2018 Article en page(s) : pp 400 - 411 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] extraction d'arbres [Termes IGN] forêt [Termes IGN] houppier [Termes IGN] Ontario (Canada) [Termes IGN] Pinophyta [Termes IGN] segmentation [Termes IGN] semis de points Résumé : (Auteur) Characterization of individual trees is essential for many applications in forest management and ecology. Previous studies relied on single tree detection from monochromatic wavelength airborne laser scanning (ALS) systems and they focused on the use of the geometric spatial information of the point clouds (i.e., X, Y, and Z coordinates). However, there is quite often a difficulty dealing with clumped trees when only the geometric spatial information is considered. The emergence of multispectral LiDAR sensors provides a new solution for individual tree structure acquisition. The aim of this paper is to investigate the performance of multispectral ALS data for delineating individual trees which are challenging by using the monochromatic wavelength ALS system. The proposed workflow utilizes the mean shift segmentation method on different feature spaces for crown isolation. In addition, both spatial domain and multispectral domain are used to refine the under-segmentation crown segments. Ten plots (2 sets of different structural complexity) located in the dense coniferous forest area in Tobermory, Ontario, Canada are selected as experiment data. Results show that the developed method correctly detects 88% and 82% of the dominant trees with and without multispectral information, respectively. Compared with segmentation using geometric spatial information solely, the main improvements are achieved for clumped tree segment with the distinguished multispectral features. This study demonstrates that multispectral airborne laser scanning data is more capable for individual tree delineation than monochromatic wavelength laser scanning data in dealing with forests with clumped crowns in dense forests. Numéro de notice : A2018-404 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.08.010 date de publication en ligne : 17/08/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.08.010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90862 [article]

Réservation

Réserver ce document

Exemplaires (3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2018101	RAB	Revue	Centre de documentation	En réserve 3L	Disponible
081-2018103	DEP-EXM	Revue	LaSTIG	Dépôt en unité	Exclu du prêt
081-2018102	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt

3D local feature BKD to extract road information from mobile laser scanning point clouds / Yang Bisheng in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 130 (August 2017) . - pp 329 - 343 Titre : 3D local feature BKD to extract road information from mobile laser scanning point clouds Type de document : Article/Communication Auteurs : Yang Bisheng, Auteur ; Yuan Liu, Auteur ; Zhen Dong, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 329 - 343 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] classificateur [Termes IGN] classification par forêts aléatoires [Termes IGN] densité des points [Termes IGN] données localisées 3D [Termes IGN] estimation par noyau [Termes IGN] extraction du réseau routier [Termes IGN] semis de points [Termes IGN] télémétrie laser mobile [Termes IGN] variable binaire Résumé : (Auteur) Extracting road information from point clouds obtained through mobile laser scanning (MLS) is essential for autonomous vehicle navigation, and has hence garnered a growing amount of research interest in recent years. However, the performance of such systems is seriously affected due to varying point density and noise. This paper proposes a novel three-dimensional (3D) local feature called the binary kernel descriptor (BKD) to extract road information from MLS point clouds. The BKD consists of Gaussian kernel density estimation and binarization components to encode the shape and intensity information of the 3D point clouds that are fed to a random forest classifier to extract curbs and markings on the road. These are then used to derive road information, such as the number of lanes, the lane width, and intersections. In experiments, the precision and recall of the proposed feature for the detection of curbs and road markings on an urban dataset and a highway dataset were as high as 90%, thus showing that the BKD is accurate and robust against varying point density and noise. Numéro de notice : A2017-517 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.06.007 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.06.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86479 [article]

Réservation

Réserver ce document

Exemplaires (3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2017081	RAB	Revue	Centre de documentation	En réserve 3L	Disponible
081-2017083	DEP-EXM	Revue	LaSTIG	Dépôt en unité	Exclu du prêt
081-2017082	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt

An automated method to register airborne and terrestrial laser scanning point clouds / Bisheng Yang in ISPRS Journal of photogrammetry and remote sensing, vol 109 (November 2015)  

Permalink

Hierarchical extraction of urban objects from mobile laser scanning data / Bisheng Yang in ISPRS Journal of photogrammetry and remote sensing, vol 99 (January 2015)  

Permalink

A shape-based segmentation method for mobile laser scanning point clouds / Yang Bisheng in ISPRS Journal of photogrammetry and remote sensing, vol 81 (July 2013)

Permalink