Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-5, 529-536, 2014
© Author(s) 2014. This work is distributed
under the Creative Commons Attribution 3.0 License.
06 Jun 2014
Evaluation of DEM generation accuracy from UAS imagery
M. Santise, M. Fornari, G. Forlani, and R. Roncella DICATeA, Università di Parma, Viale delle Scienze 181/a, 43124 Parma, Italy
Keywords: UAS, Photogrammetry, Cartography, Accuracy, DEM/DTM, Comparison Abstract. The growing use of UAS platform for aerial photogrammetry comes with a new family of Computer Vision highly automated processing software expressly built to manage the peculiar characteristics of these blocks of images. It is of interest to photogrammetrist and professionals, therefore, to find out whether the image orientation and DSM generation methods implemented in such software are reliable and the DSMs and orthophotos are accurate. On a more general basis, it is interesting to figure out whether it is still worth applying the standard rules of aerial photogrammetry to the case of drones, achieving the same inner strength and the same accuracies as well. With such goals in mind, a test area has been set up at the University Campus in Parma. A large number of ground points has been measured on natural as well as signalized points, to provide a comprehensive test field, to check the accuracy performance of different UAS systems. In the test area, points both at ground-level and features on the buildings roofs were measured, in order to obtain a distributed support also altimetrically. Control points were set on different types of surfaces (buildings, asphalt, target, fields of grass and bumps); break lines, were also employed. The paper presents the results of a comparison between two different surveys for DEM (Digital Elevation Model) generation, performed at 70 m and 140 m flying height, using a Falcon 8 UAS.
Conference paper (PDF, 843 KB)

Citation: Santise, M., Fornari, M., Forlani, G., and Roncella, R.: Evaluation of DEM generation accuracy from UAS imagery, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-5, 529-536, doi:10.5194/isprsarchives-XL-5-529-2014, 2014.

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