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Weighted spherical sampling of point clouds for forested scenes / Alex Fafard in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 10 (October 2020)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 10 (October 2020) . - pp 619 - 625 Titre : Weighted spherical sampling of point clouds for forested scenes Type de document : Article/Communication Auteurs : Alex Fafard, Auteur ; Ali Rouzbeh Kargar, Auteur ; Jan Van Aardt, Auteur Année de publication : 2020 Article en page(s) : pp 619 - 625 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] coordonnées sphériques [Termes IGN] densité de la végétation [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] données lidar [Termes IGN] échantillonnage [Termes IGN] mangrove [Termes IGN] Micronésie [Termes IGN] scène forestière [Termes IGN] semis de points [Termes IGN] volume en bois Résumé : (Auteur) Terrestrial laser scanning systems are characterized by a sampling pattern which varies in point density across the hemisphere. Additionally, close objects are over-sampled relative to objects that are farther away. These two effects compound to potentially bias the three-dimensional statistics of measured scenes. Previous methods of sampling have resulted in a loss of structural coherence. In this article, a method of sampling is proposed to optimally sample points while preserving the structure of a scene. Points are sampled along a spherical coordinate system, with probabilities modulated by elevation angle and squared distance from the origin. The proposed approach is validated through visual comparison and stem-volume assessment in a challenging mangrove forest in Micronesia. Compared to several well-known sampling techniques, the proposed approach reduces sampling bias and shows strong performance in stem-reconstruction measurement. The proposed sampling method matched or exceeded the stem-volume measurement accuracy across a variety of tested decimation levels. On average it achieved 3.0% higher accuracy at estimating stem volume than the closest competitor. This approach shows promise for improving the evaluation of terrestrial laser-scanning data in complex scenes. Numéro de notice : A2020-493 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.10.619 Date de publication en ligne : 01/10/2020 En ligne : https://doi.org/10.14358/PERS.86.10.619 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96093 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-2020101	SL	Revue	Centre de documentation	Revues en salle	Disponible

A novel automatic method for the fusion of ALS and TLS LiDAR data for robust assessment of tree crown structure / Claudia Paris in IEEE Transactions on geoscience and remote sensing, vol 55 n° 7 (July 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 7 (July 2017) . - pp 3679 - 3693 Titre : A novel automatic method for the fusion of ALS and TLS LiDAR data for robust assessment of tree crown structure Type de document : Article/Communication Auteurs : Claudia Paris, Auteur ; David Kelbe, Auteur ; Jan Van Aardt, Auteur ; Lorenzo Bruzzone, Auteur Année de publication : 2017 Article en page(s) : pp 3679 - 3693 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] canopée [Termes IGN] corrélation croisée normalisée [Termes IGN] extraction de traits caractéristiques [Termes IGN] fusion d'images [Termes IGN] semis de points [Termes IGN] télémétrie laser aéroporté [Termes IGN] télémétrie laser sur satellite Résumé : (Auteur) Tree crown structural parameters are key inputs to studies spanning forest fire propagation, invasive species dynamics, avian habitat provision, and so on, but these parameters consistently are difficult to measure. While airborne laser scanning (ALS) provides uniform data and a consistent nadir perspective necessary for crown segmentation, the data characteristics of terrestrial laser scanning (TLS) make such crown segmentation efforts much more challenging. We present a data fusion approach to extract crown structure from TLS, by exploiting the complementary perspective of ALS. Multiple TLS point clouds are automatically registered to a single ALS point cloud by maximizing the normalized cross correlation between the global ALS canopy height model (CHM) and each of the local TLS CHMs through parameter optimization of a planar Euclidean transform. Per-tree canopy segmentation boundaries, which are reliably obtained from ALS, can then be adapted onto the more irregular TLS data. This is repeated for each TLS scan; the combined segmentation results from each registered TLS scan and the ALS data are fused into a single per-tree point cloud, from which canopy-level structural parameters readily can be extracted. Numéro de notice : A2017-485 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2675963 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2675963 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86407 [article]

Multiview marker-free registration of forest terrestrial laser scanner data with embedded confidence metrics / David Kelbe in IEEE Transactions on geoscience and remote sensing, vol 55 n° 2 (February 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 2 (February 2017) . - pp 729 - 741 Titre : Multiview marker-free registration of forest terrestrial laser scanner data with embedded confidence metrics Type de document : Article/Communication Auteurs : David Kelbe, Auteur ; Jan Van Aardt, Auteur ; Paul Romanczyk, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 729 - 741 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] acquisition de données [Termes IGN] carte de confiance [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] forêt [Termes IGN] mesure géométrique [Termes IGN] New York (Etats-Unis ; état) [Termes IGN] numérisation [Termes IGN] semis de points [Termes IGN] structure d'un peuplement forestier [Termes IGN] superposition Résumé : (Auteur) Terrestrial laser scanning has demonstrated increasing potential for rapid comprehensive measurement of forest structure, especially when multiple scans are spatially registered in order to reduce the limitations of occlusion. Although marker-based registration techniques (based on retro-reflective spherical targets) are commonly used in practice, a blind marker-free approach is preferable, insofar as it supports rapid operational data acquisition. To support these efforts, we extend the pairwise registration approach of our earlier work, and develop a graph-theoretical framework to perform blind marker-free global registration of multiple point cloud data sets. Pairwise pose estimates are weighted based on their estimated error, in order to overcome pose conflict while exploiting redundant information and improving precision. The proposed approach was tested for eight diverse New England forest sites, with 25 scans collected at each site. Quantitative assessment was provided via a novel embedded confidence metric, with a mean estimated root-mean-square error of 7.2 cm and 89% of scans connected to the reference node. This paper assesses the validity of the embedded multiview registration confidence metric and evaluates the performance of the proposed registration algorithm. Numéro de notice : A2017-142 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2614251 En ligne : https://doi.org/10.1109/TGRS.2016.2614251 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84630 [article]

On the fusion of lidar and aerial color imagery to detect urban vegetation and buildings / Madhurima Bandyopadhyay in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 2 (February 2017)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 2 (February 2017) . - pp 123 - 136 Titre : On the fusion of lidar and aerial color imagery to detect urban vegetation and buildings Type de document : Article/Communication Auteurs : Madhurima Bandyopadhyay, Auteur ; Jan Van Aardt, Auteur ; Kerry Cawse-Nicholson, Auteur ; Emmett Lentilucci, Auteur Année de publication : 2017 Article en page(s) : pp 123 - 136 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] détection du bâti [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] extraction de la végétation [Termes IGN] fusion de données [Termes IGN] image aérienne [Termes IGN] image en couleur [Termes IGN] image RVB [Termes IGN] zone urbaine Résumé : (Auteur) Three-dimensional (3D) data from light detection and ranging (lidar) sensor have proven advantageous in the remote sensing domain for characterization of object structure and dimensions. Fusion-based approaches of lidar and aerial imagery also becoming popular. In this study, aerial color (RGB) imagery, along with co-registered airborne discrete lidar data were used to separate vegetation and buildings from other urban classes/cover-types, as a precursory step towards the assessment of urban forest biomass. Both spectral and structural features such as object height, distribution of surface normals from the lidar, and a novel vegetation metric derived from combined lidar and RGB imagery, referred to as the lidar-infused vegetation index (LDVI) were used in this classification method. The proposed algorithm was tested on different cityscape regions to verify its robustness. Results showed a good separation of buildings and vegetation from other urban classes with on average an overall classification accuracy of 92 percent, with a kappa statistic of 0.85. These results bode well for the operational fusion of lidar and RGB imagery, often flown on the same platform, towards improved characterization of the urban forest and built environments. Numéro de notice : A2017-039 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.2.123 En ligne : https://doi.org/10.14358/PERS.83.2.123 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84140 [article]

3D tree reconstruction from simulated small footprint waveform lidar / Jiaying Wu in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 12 (December 2013)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 79 n° 12 (December 2013) . - pp 1147 - 1157 Titre : 3D tree reconstruction from simulated small footprint waveform lidar Type de document : Article/Communication Auteurs : Jiaying Wu, Auteur ; Kerry Cawse-Nicholson, Auteur ; Jan Van Aardt, Auteur Année de publication : 2013 Article en page(s) : pp 1147 - 1157 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] arbre (flore) [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] feuille (végétation) [Termes IGN] forme d'onde [Termes IGN] houppier [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] Populus (genre) [Termes IGN] reconstruction d'objet [Termes IGN] squelettisation [Termes IGN] tronc [Termes IGN] variation saisonnière Résumé : (Auteur) Lidar-based 3D tree reconstruction enables the retrieval of detailed tree structure; however, many existing methods are based on high-density discrete return lidar datasets. In this paper, we propose the use of small footprint waveform lidar data to achieve branch-level tree reconstruction for both leaf-off and leaf-on conditions. The DIRSIG simulation environment was used for algorithm validation purposes. Leaf-off data served as reference, and leaf-on reconstruction for a particular tree resulted in an average branch length difference of 0.07 m and an average angular difference of approximately 6 degrees for both tilt and azimuth angles. Compared to in situ methods this approach may be used by an airborne system for accurate estimation of forest biomass, forest inventory, land degradation, etc. in large scale applications. Furthermore, since this approach can also be applied on leaf-on trees, the tree skeleton characterization eventually can be conducted year round and will be less dependent on seasonal changes. Numéro de notice : A2013-691 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14358/PERS.79.12.1147 En ligne : https://doi.org/10.14358/PERS.79.12.1147 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32827 [article]

A robust signal preprocessing chain for small-footprint waveform LiDAR / J. Wu in IEEE Transactions on geoscience and remote sensing, vol 50 n° 8 (August 2012)  

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Geospatial disaster response during the Haiti earthquake : A case study spanning airborne deployment, data collection, transfer, processing, and dissemination / Jan Van Aardt in Photogrammetric Engineering & Remote Sensing, PERS, vol 77 n° 9 (September 2011)  

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