Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W4, 351-354, 2015
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-1-W4/351/2015/
doi:10.5194/isprsarchives-XL-1-W4-351-2015
© Author(s) 2015. This work is distributed
under the Creative Commons Attribution 3.0 License.
 
27 Aug 2015
AUTOMATED DSM EXTRACTION FROM UAV IMAGES AND PERFORMANCE ANALYSIS
S. Rhee1 and T. Kim2 13DLabs Co. Ltd., Incheon, South Korea
2Dept. of Geoinformatic Engineering, Inha University, Incheon, South Korea
Keywords: DSM, Stereo matching, Epipolar lines, UAV, Photogrammetry Abstract. As technology evolves, unmanned aerial vehicles (UAVs) imagery is being used from simple applications such as image acquisition to complicated applications such as 3D spatial information extraction. Spatial information is usually provided in the form of a DSM or point cloud. It is important to generate very dense tie points automatically from stereo images. In this paper, we tried to apply stereo image-based matching technique developed for satellite/aerial images to UAV images, propose processing steps for automated DSM generation and to analyse the possibility of DSM generation. For DSM generation from UAV images, firstly, exterior orientation parameters (EOPs) for each dataset were adjusted. Secondly, optimum matching pairs were determined. Thirdly, stereo image matching was performed with each pair. Developed matching algorithm is based on grey-level correlation on pixels applied along epipolar lines. Finally, the extracted match results were united with one result and the final DSM was made. Generated DSM was compared with a reference DSM from Lidar. Overall accuracy was 1.5 m in NMAD. However, several problems have to be solved in future, including obtaining precise EOPs, handling occlusion and image blurring problems. More effective interpolation technique needs to be developed in the future.
Conference paper (PDF, 1137 KB)


Citation: Rhee, S. and Kim, T.: AUTOMATED DSM EXTRACTION FROM UAV IMAGES AND PERFORMANCE ANALYSIS, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W4, 351-354, doi:10.5194/isprsarchives-XL-1-W4-351-2015, 2015.

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