Generating High-Resolution DSM often demands highly accurate data. Among the range of terrestrial and aerial methods available to produce such a dataset, this project tests the utility of images acquired by a fixed-wing, low-cost Unmanned Aerial Vehicle (UAV). The data processing of UAV images has been carried out using the algorithms ranging from classical photogrammetry to modern Computer Vision (CV) algorithms.

Nowadays, the use of UAV has increased to offer a fast, easy and cost-effective way to capture high-resolution images for a comparatively smaller area. In this paper, we present a state-of-the-art photogrammetry and image processing techniques provided by different software and their algorithms. The key element in a UAV photogrammetric data processing of the images which have been obtained with large variation in its geometry is the accurate georeferencing.

27 high resolution (2.4 cm average spatial resolution) UAV-acquired images of sand mine at Tielt-Winge, Belgium has been used for this project. All the images have been acquired by a Sony Nex-5R digital camera mounted on a Trimble UX5 Imaging Rover, a fixed-wing UAV. Although three software: LPS, AgiSoft PhotoScan, and PIX4D were used for image processing. The identified algorithms and limitations in the processing are valid for most other commercial photogrammetric software available on the market.

The effort and the achievable accuracy of DSM result from every process are compared using the highly accurate ground control points as the reference data. The comparison of the DSM is performed through the difference of DSM, RMSE, and visual interpretation and to conclude SIFT algorithm and Dense Stereo matching provide a good result for UAV data compared to traditional digital image matching using Forstner interest operator and ultimately provided centimeter-level accuracy in the output results. However, for certain areas like forest and vegetation, poor results were obtained due to poor image matching.

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The slides for the paper prepared at the university.