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Automatic registration of optical aerial imagery to a LiDAR point cloud for generation of city models / Bernard O. Abayowa in ISPRS Journal of photogrammetry and remote sensing, vol 106 (August 2015)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 106 (August 2015) . - pp 68 - 81 Titre : Automatic registration of optical aerial imagery to a LiDAR point cloud for generation of city models Type de document : Article/Communication Auteurs : Bernard O. Abayowa, Auteur ; Alper Yilmaz, Auteur ; Russell C. Hardie, Auteur Année de publication : 2015 Article en page(s) : pp 68 - 81 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] algorithme ICP [Termes IGN] corrélation croisée normalisée [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] image aérienne [Termes IGN] image optique [Termes IGN] méthode robuste [Termes IGN] milieu urbain [Termes IGN] modèle 3D de l'espace urbain [Termes IGN] modèle numérique de surface [Termes IGN] reconstruction 3D [Termes IGN] scène [Termes IGN] semis de points [Termes IGN] superposition d'images Résumé : (auteur) This paper presents a framework for automatic registration of both the optical and 3D structural information extracted from oblique aerial imagery to a Light Detection and Ranging (LiDAR) point cloud without prior knowledge of an initial alignment. The framework employs a coarse to fine strategy in the estimation of the registration parameters. First, a dense 3D point cloud and the associated relative camera parameters are extracted from the optical aerial imagery using a state-of-the-art 3D reconstruction algorithm. Next, a digital surface model (DSM) is generated from both the LiDAR and the optical imagery-derived point clouds. Coarse registration parameters are then computed from salient features extracted from the LiDAR and optical imagery-derived DSMs. The registration parameters are further refined using the iterative closest point (ICP) algorithm to minimize global error between the registered point clouds. The novelty of the proposed approach is in the computation of salient features from the DSMs, and the selection of matching salient features using geometric invariants coupled with Normalized Cross Correlation (NCC) match validation. The feature extraction and matching process enables the automatic estimation of the coarse registration parameters required for initializing the fine registration process. The registration framework is tested on a simulated scene and aerial datasets acquired in real urban environments. Results demonstrates the robustness of the framework for registering optical and 3D structural information extracted from aerial imagery to a LiDAR point cloud, when co-existing initial registration parameters are unavailable. Numéro de notice : A2015-722 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.05.006 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.05.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78369 [article]

Modeling aboveground tree woody biomass using national-scale allometric methods and airborne lidar / Qi Chen in ISPRS Journal of photogrammetry and remote sensing, vol 106 (August 2015)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 106 (August 2015) . - pp 95 - 106 Titre : Modeling aboveground tree woody biomass using national-scale allometric methods and airborne lidar Type de document : Article/Communication Auteurs : Qi Chen, Auteur Année de publication : 2015 Article en page(s) : pp 95 - 106 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] allométrie [Termes IGN] biomasse forestière [Termes IGN] changement climatique [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] hauteur des arbres [Termes IGN] lasergrammétrie [Termes IGN] modèle numérique [Termes IGN] puits de carbone [Termes IGN] tronc [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Estimating tree aboveground biomass (AGB) and carbon (C) stocks using remote sensing is a critical component for understanding the global C cycle and mitigating climate change. However, the importance of allometry for remote sensing of AGB has not been recognized until recently. The overarching goals of this study are to understand the differences and relationships among three national-scale allometric methods (CRM, Jenkins, and the regional models) of the Forest Inventory and Analysis (FIA) program in the U.S. and to examine the impacts of using alternative allometry on the fitting statistics of remote sensing-based woody AGB models. Airborne lidar data from three study sites in the Pacific Northwest, USA were used to predict woody AGB estimated from the different allometric methods. It was found that the CRM and Jenkins estimates of woody AGB are related via the CRM adjustment factor. In terms of lidar-biomass modeling, CRM had the smallest model errors, while the Jenkins method had the largest ones and the regional method was between. The best model fitting from CRM is attributed to its inclusion of tree height in calculating merchantable stem volume and the strong dependence of non-merchantable stem biomass on merchantable stem biomass. This study also argues that it is important to characterize the allometric model errors for gaining a complete understanding of the remotely-sensed AGB prediction errors. Numéro de notice : A2015-723 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.05.007 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.05.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78371 [article]

Full-waveform data for building roof step edge localization / Małgorzata Słota in ISPRS Journal of photogrammetry and remote sensing, vol 106 (August 2015)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 106 (August 2015) . - pp 129 - 144 Titre : Full-waveform data for building roof step edge localization Type de document : Article/Communication Auteurs : Małgorzata Słota, Auteur Année de publication : 2015 Article en page(s) : pp 129 - 144 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] détection de contours [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] forme d'onde pleine [Termes IGN] modélisation du bâti [Termes IGN] signal laser [Termes IGN] toit Résumé : (auteur) Airborne laser scanning data perfectly represent flat or gently sloped areas; to date, however, accurate breakline detection is the main drawback of this technique. This issue becomes particularly important in the case of modeling buildings, where accuracy higher than the footprint size is often required. This article covers several issues related to full-waveform data registered on building step edges. First, the full-waveform data simulator was developed and presented in this paper. Second, this article provides a full description of the changes in echo amplitude, echo width and returned power caused by the presence of edges within the laser footprint. Additionally, two important properties of step edge echoes, peak shift and echo asymmetry, were noted and described. It was shown that these properties lead to incorrect echo positioning along the laser center line and can significantly reduce the edge points’ accuracy. For these reasons and because all points are aligned with the center of the beam, regardless of the actual target position within the beam footprint, we can state that step edge points require geometric corrections. This article presents a novel algorithm for the refinement of step edge points. The main distinguishing advantage of the developed algorithm is the fact that none of the additional data, such as emitted signal parameters, beam divergence, approximate edge geometry or scanning settings, are required. The proposed algorithm works only on georeferenced profiles of reflected laser energy. Another major advantage is the simplicity of the calculation, allowing for very efficient data processing. Additionally, the developed method of point correction allows for the accurate determination of points lying on edges and edge point densification. For this reason, fully automatic localization of building roof step edges based on LiDAR full-waveform data with higher accuracy than the size of the lidar footprint is feasible. Numéro de notice : A2015-724 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.05.008 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.05.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78372 [article]