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HYBRID GEOREFERENCING, ENHANCEMENT AND CLASSIFICATION OF ULTRA-HIGH RESOLUTION UAV LIDAR AND IMAGE POINT CLOUDS FOR MONITORING APPLICATIONS

التفاصيل البيبلوغرافية
العنوان: HYBRID GEOREFERENCING, ENHANCEMENT AND CLASSIFICATION OF ULTRA-HIGH RESOLUTION UAV LIDAR AND IMAGE POINT CLOUDS FOR MONITORING APPLICATIONS
المؤلفون: N. Haala, M. Kölle, M. Cramer, D. Laupheimer, G. Mandlburger, P. Glira
المصدر: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol V-2-2020, Pp 727-734 (2020)
بيانات النشر: Copernicus Publications, 2020.
سنة النشر: 2020
المجموعة: LCC:Technology
LCC:Engineering (General). Civil engineering (General)
LCC:Applied optics. Photonics
مصطلحات موضوعية: Technology, Engineering (General). Civil engineering (General), TA1-2040, Applied optics. Photonics, TA1501-1820
الوصف: This paper presents a study on the potential of ultra-high accurate UAV-based 3D data capture by combining both imagery and LiDAR data. Our work is motivated by a project aiming at the monitoring of subsidence in an area of mixed use. Thus, it covers built-up regions in a village with a ship lock as the main object of interest as well as regions of agricultural use. In order to monitor potential subsidence in the order of 10 mm/year, we aim at sub-centimeter accuracies of the respective 3D point clouds. We show that hybrid georeferencing helps to increase the accuracy of the adjusted LiDAR point cloud by integrating results from photogrammetric block adjustment to improve the time-dependent trajectory corrections. As our main contribution, we demonstrate that joint orientation of laser scans and images in a hybrid adjustment framework significantly improves the relative and absolute height accuracies. By these means, accuracies corresponding to the GSD of the integrated imagery can be achieved. Image data can also help to enhance the LiDAR point clouds. As an example, integrating results from Multi-View Stereo potentially increases the point density from airborne LiDAR. Furthermore, image texture can support 3D point cloud classification. This semantic segmentation discussed in the final part of the paper is a prerequisite for further enhancement and analysis of the captured point cloud.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2194-9042
2194-9050
Relation: https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-2-2020/727/2020/isprs-annals-V-2-2020-727-2020.pdf; https://doaj.org/toc/2194-9042; https://doaj.org/toc/2194-9050
DOI: 10.5194/isprs-annals-V-2-2020-727-2020
URL الوصول: https://doaj.org/article/dc2612918e414621b9e8632bec6badaa
رقم الأكسشن: edsdoj.2612918e414621b9e8632bec6badaa
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:21949042
21949050
DOI:10.5194/isprs-annals-V-2-2020-727-2020