Digital Terrain Models suitability for Helicopter Landing Sites Identification

The problem of improving the accuracy of digital terrain models created for monitoring and diagnostics of the railway track and the surrounding area is considered. A technical solution to this problem is presented, which includes a method for joint aerial photography and laser scanning, as well as a method for digital processing of the obtained data. The relevance of using this solution is due to the existence of zones of weak reception of signals from the global navigation satellite system, since in these zones the accuracy of constructing digital terrain models using currently used diagnostic spatial scanning systems is reduced. The technical solution is based on the method of digital processing of aerial photographs of the railway track. In this case, as elements of external orientation, the threads of the rail track located at a normalized distance from each other are used. The use of this method made it possible to increase the accuracy of determining the flight path of an aircraft over railway tracks and, as a result, the accuracy of calculating the coordinates of points on the earth's surface. As a result, a digital terrain model was created that is suitable for diagnostics and monitoring the condition of the railway trackbed. During simulation modeling, it was found that the application of the proposed method allowed to reduce to 50 % the confidence interval of the distribution of the error in determining the coordinates of points on the terrain and increase the accuracy of forming a digital terrain model. This promising technical solution for improving the accuracy of digital terrain models for railway track diagnostics is implemented using unmanned aerial vehicles that are part of the mobile diagnostic complex. The advantages of the proposed solution include high efficiency and availability of application.

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Accuracy Assessment of Point Clouds from LiDAR and Dense Image Matching Acquired Using the UAV Platform for DTM Creation

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi7090342 ◽

2018 ◽

Vol 7 (9) ◽

pp. 342 ◽

Cited By ~ 28

Author(s):

Adam Salach ◽

Krzysztof Bakuła ◽

Magdalena Pilarska ◽

Wojciech Ostrowski ◽

Konrad Górski ◽

...

Keyword(s):

Land Cover ◽

Laser Scanning ◽

Point Clouds ◽

Data Sources ◽

Digital Terrain Models ◽

Vegetation Height ◽

Digital Terrain ◽

Terrain Models ◽

Airborne Laser ◽

Vertical Accuracy

In this paper, the results of an experiment about the vertical accuracy of generated digital terrain models were assessed. The created models were based on two techniques: LiDAR and photogrammetry. The data were acquired using an ultralight laser scanner, which was dedicated to Unmanned Aerial Vehicle (UAV) platforms that provide very dense point clouds (180 points per square meter), and an RGB digital camera that collects data at very high resolution (a ground sampling distance of 2 cm). The vertical error of the digital terrain models (DTMs) was evaluated based on the surveying data measured in the field and compared to airborne laser scanning collected with a manned plane. The data were acquired in summer during a corridor flight mission over levees and their surroundings, where various types of land cover were observed. The experiment results showed unequivocally, that the terrain models obtained using LiDAR technology were more accurate. An attempt to assess the accuracy and possibilities of penetration of the point cloud from the image-based approach, whilst referring to various types of land cover, was conducted based on Real Time Kinematic Global Navigation Satellite System (GNSS-RTK) measurements and was compared to archival airborne laser scanning data. The vertical accuracy of DTM was evaluated for uncovered and vegetation areas separately, providing information about the influence of the vegetation height on the results of the bare ground extraction and DTM generation. In uncovered and low vegetation areas (0–20 cm), the vertical accuracies of digital terrain models generated from different data sources were quite similar: for the UAV Laser Scanning (ULS) data, the RMSE was 0.11 m, and for the image-based data collected using the UAV platform, it was 0.14 m, whereas for medium vegetation (higher than 60 cm), the RMSE from these two data sources were 0.11 m and 0.36 m, respectively. A decrease in the accuracy of 0.10 m, for every 20 cm of vegetation height, was observed for photogrammetric data; and such a dependency was not noticed in the case of models created from the ULS data.

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