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LiDAR strip adjustment using multifeatures matched with aerial images / Yongjun Zhang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 2 (February 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 2 (February 2015) . - pp 976 - 987 Titre : LiDAR strip adjustment using multifeatures matched with aerial images Type de document : Article/Communication Auteurs : Yongjun Zhang, Auteur ; Xiaodong Xiong, Auteur ; Maoteng Zheng, Auteur ; Xu Huang, Auteur Année de publication : 2015 Article en page(s) : pp 976 - 987 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] aérotriangulation numérique [Termes IGN] angle de visée [Termes IGN] compensation par bloc [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] erreur systématique [Termes IGN] image aérienne [Termes IGN] ligne de visée [Termes IGN] positionnement automatique [Termes IGN] semis de points Résumé : (Auteur) Airborne light detecting and ranging (LiDAR) systems have been widely used for the fast acquisition of dense topographic data. Regrettably, coordinate errors always exist in LiDAR-acquired points. The errors are attributable to several sources, such as laser ranging errors, sensor mounting errors, and position and orientation system (POS) systematic errors, among others. LiDAR strip adjustment (LSA) is the solution to eliminating the errors, but most state-of-the-art LSA methods neglect the influence from POS systematic errors by assuming that the POS is precise enough. Unfortunately, many of the LiDAR systems used in China are equipped with a low-precision POS due to cost considerations. Subsequently, POS systematic errors should be also considered in the LSA. This paper presents an aerotriangulation-aided LSA (AT-aided LSA) method whose major task is eliminating position and angular errors of the laser scanner caused by boresight angular errors and POS systematic errors. The aerial images, which cover the same area with LiDAR strips, are aerotriangulated and serve as the reference data for LSA. Two types of conjugate features are adopted as control elements (i.e., the conjugate points matched between the LiDAR intensity images and the aerial images and the conjugate corner features matched between LiDAR point clouds and aerial images). Experiments using the AT-aided LSA method are conducted using a real data set, and a comparison with the three-dimensional similarity transformation (TDST) LSA method is also performed. Experimental results support the feasibility of the proposed AT-aided LSA method and its superiority over the TDST LSA method. Numéro de notice : A2015-111 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2331234 En ligne : https://doi.org/10.1109/TGRS.2014.2331234 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75629 [article]

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