• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.6
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• pp.535-544
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• 2017

UAVs (Unmanned Aerial Vehicle) are generally classified into fixed-wing and rotary-wing type, and both have very different flight characteristics each other during photographing. These can greatly effect on the quality of images and their productions. In this paper, the change of the camera rotation angle at the moment of photographing was compared and analyzed by calculating orientation angles of each image taken by both types of payload. Study materials were acquired at an altitude of 130m and 260m with fixed-wing, and at an altitude of 130m with rotary-wing UAV over an agricultural land. In addition, an accuracy comparison of boundary surveying methods between UAV photogrammetry and terrestrial cadastral surveying was conducted in two parcels of the study area. The study results are summarized as follows. The differences at rotation angles of images acquired with between two types of UAVs at the same flight height of 130m were significantly very large. On the other hand, the distance errors of parcel boundary surveying were not significant between them, but almost the same, about within ${\pm}0.075m$ in RMSE (Root Mean Square Error). The accuracy of boundary surveying with a fixed-wing UAV at 260m altitude was quite variable, $0.099{\sim}0.136m$ in RMSE. In addition, the error of area extracted from UAV-orthoimages was less than 0.2% compared with the results of the cadastral survey in the same two parcels used for the boundary surveying, In conclusion, UAV photogrammetry can be highly utilized in the field of cadastral surveying.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.9
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• pp.429-436
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• 2017

Unmanned Aerial Vehicles (UAV) that are piloted without human pilots can be commanded remotely via frequencies or perform pre-inputted missions. UAVs have been mainly used for military purposes, but due to the development of ICT technology, they are now widely used in the private sector. Teal Group's 2014 World UAV Forecast predicts that the UAV market will grow by 10% annually over the next decade, reaching $ 12.5 billion by 2023. However, because UAVs are primarily remotely controlled, if a malicious user accesses a remotely controlled UAV, it could seriously infringe privacy and cause financial loss or even loss of life. To solve this problem, a secure channel must be established through mutual authentication between the UAV and the control center. However, existing security techniques require a lot of computing resources and power, and because communication distances, infrastructure, and data flow are different from UAV networks, it is unsuitable for application in UAV environments. To resolve this problem, the study presents a lightweight UAV authentication method based on Physical Unclonable Functions (PUFs) that requires less computing resources in the ground Control and Non-Payload Communication (CNPC) environment, where recently, technology standardization is actively under progress.

• Journal of Cadastre & Land InformatiX
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• v.47 no.1
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• pp.15-26
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• 2017

The decrease of green space according to the urbanization has caused many environmental problems as the destruction of habitat, air pollution, heat island effect. With interest growing in natural view recently, proper management of evergreen tree which is lived even the winter season has been on the rise importantly. This study analyzed the distribution area of evergreen tree using vegetation index based on unmanned aerial vehicle (UAV). Firstly, RGB and NIR+RG camera were loaded in fixed-wing UAV and image mosaic was achieved using GCPs based on Pix4d SW. And normalized differences vegetation index (NDVI) and soil adjusted vegetation index (SAVI) was calculated by band math function from acquired ortho mosaic image. validation points were applied to evaluate accuracy of the distribution of evergreen tree for each range value and analysis showed that kappa coefficient marked the highest as 0.822 and 0.816 respectively in "NDVI > 0.5" and "SAVI > 0.7". The area of evergreen tree in "NDVI > 0.5" and "SAVI > 0.7" was $11,824m^2$ and $15,648m^2$ respectively, that was ratio of 4.8% and 6.3% compared to total area. It was judged that UAV could supply the latest and high resolution information to vegetation works as urban environment, air pollution, climate change, and heat island effect.

• Journal of Cadastre & Land InformatiX
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• v.48 no.1
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• pp.5-14
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• 2018

Recently the construction of solar plant showed a steady growth in influence of renewable energy policy. It is very important to determine the optimal location and aspect of solar panel using analyzed data of solar radiation to solar plant area beforehand. This study analyzed solar radiation in solar plant area using DEM acquired from UAV geospatial information. Mean solar radiation of 2017 was calculated as $1,474,466W/m^2$ and total solar radiation of 2017 considering solar plant area showed $33,639MW/m^2$ on analyzed result. It is important to analyze monthly solar radiation in aspect of maintenance works of solar plant. Monthly solar radiation of May to July was calculated over $160,000W/m^2$ and that of January to February and November to December showed under $80,000W/m^2$ in monthly solar radiation analysis of solar plant area. Also this study compared with solar radiation being calculated from UAV geospatial information and that of National Institute of Meteorological Sciences. And mean solar radiation of study area showed a little high in comparison with whole country data of National Institute of Meteorological Sciences, because the 93.7% of study area was composed of south aspect. Therefore this study can be applied to calculate solar radiation in new developed solar plant area very quickly using UAV.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.1
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• pp.81-87
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• 2016

It is important to analyze the exact damage information for fast recovery when natural disasters cause damage on river-side facilities such as dams, bridges, embankments etc. In this study, we shows the method to effectively damage analysis plan using UAV(Unmanned aerial vehicle) images and accuracy assessment of it. The UAV images are captured on area near the river-side facilities and the core methodology for damage analysis are image matching and change detection algorithm. The result(point cloud) from image matching is to construct 3-dimensional data using by 2-dimensional images, it extracts damage areas by comparing the height values on same area with reference data. The results are tested absolute locational precision compared by post-processed aerial LiDAR data named reference data. The assessment analysis test shows our matching results 10-20 centimeter level precision if external orientation parameters are very accurate. This study shows suggested method is very useful for damage analysis in a large size structure like river-side facilities. But the complexity building can't apply this method, it need to the other method for damage analysis.

• Korean Journal of Remote Sensing
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• v.35 no.5_1
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• pp.705-714
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• 2019

Precision positioning is necessary for various use of high-resolution UAV images. Basically, GCP is used for this purpose, but in case of emergency situations or difficulty in selecting GCPs, the data shall be obtained without GCPs. This study proposed a method of improving positional accuracy for x, y coordinate of UAV based 3 dimensional point cloud data generated without GCPs. Road vector file by the public data (Open Data Portal) was used as reference data for improving location accuracy. The geometric correction of the 2 dimensional ortho-mosaic image was first performed and the transform matrix produced in this process was adopted to apply to the 3 dimensional point cloud data. The straight distance difference of 34.54 m before the correction was reduced to 1.21 m after the correction. By confirming that it is possible to improve the location accuracy of UAV images acquired without GCPs, it is expected to expand the scope of use of 3 dimensional spatial objects generated from point cloud by enabling connection and compatibility with other spatial information data.

• Journal of Advanced Navigation Technology
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• v.22 no.6
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• pp.530-536
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• 2018

Recently, many studies for interoperability of UAV in the NAS has been performed since the application range and demand of UAV are continuously increased. For the interoperation of UAV in the NAS, technical standards and certification system for UAV which is equivalent to the commercial aircraft are required and test and evaluation methodology must be presented by standards. In this paper, qualification test and evaluation methodology aboutfor the UAV navigation system is proposed. For the research, the mission profile and operation environment of UAV were analyzed. Thereafter the test criteria were derived and the test methodology were established. Finally, the simulation and demonstration using test-bed UAV were performed. As a result of the test, it was confirmed that the navigation system of test UAV has a position accuracy about 1.4 meters at 95% confidence level in the entire flight stage.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.1
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• pp.80-91
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• 2021

The purpose of this study is to present the applicability to the effective survey into the actual condition of lands such as analysis of occupied location of government owned lands based on orthoimages created from aerial photographs taken by UAV. The boundary point coordinates and areas of the parcels were observed respectively by VRS-GNSS surveying and orthoimages for each land use of two categories of land, i.e. building site and farmland. As a result of comparing boundary point coordinates and areas extracted from UAV orthoimages with VRS-GNSS surveying data which were used as reference data, the RMS error of the coordinates for the boundary points was ±0.074m for both X and Y in the building site, and ±0.150m and ±0.127m for the X and Y respectively in the farmland. The positional error of the boundary point was 1.7~ 2 times higher in the farmland than in the building site where the boundary points were relatively clear. The RMS error of ±8.964㎡ of areas in the farmland was 4.7 times higher than that of ±1.898㎡ of areas in the building site. The area errors of all 22 parcels measured from the orthoimage were found to be within the allowed error range, indicating that it is feasible to apply the orthoimage generated by UAV to survey of government owned lands in terms of accuracy.

• Journal of Advanced Navigation Technology
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• v.25 no.1
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• pp.115-123
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• 2021

Pine nuts are Korea's representative nut forest products and profitable crops. However, pine nuts are harvested by climbing the trees themselves, thus the risk is high. In order to solve this problem, it is necessary to harvest pine nuts using a robot or an unmanned aerial vehicle(UAV). In this paper, we propose a deep learning based detection method for harvesting pine nut in UAV aerial images. For this, a video was recorded in a real pine forest using UAV, and a data augmentation technique was used to supplement a small number of data. As the data for 3D detection, Unity3D was used to model the virtual pine nut and the virtual environment, and the labeling was acquired using the 3D transformation method of the coordinate system. Deep learning algorithms for detection of pine nuts distribution area and 2D and 3D detection of pine nuts objects were used DeepLabV3+, YOLOv4, and CenterNet, respectively. As a result of the experiment, the detection rate of pine nuts distribution area was 82.15%, the 2D detection rate was 86.93%, and the 3D detection rate was 59.45%.

• Journal of Korea Water Resources Association
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• v.56 no.spc1
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• pp.1027-1036
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• 2023

Coastal topographic information is crucial in coastal management, but point measurment based approeaches, which are labor intensive, are generally applied to land and underwater, separately. This study introduces an efficient method enabling land and undetwater surveys using an unmanned aerial vehicle (UAV). This method involves applying two different algorithms to measure the topography on land and water depth, respectively, using UAV imagery and merge them to reconstruct whole coastal digital elevation model. Acquisition of the landside terrain is achieved using the Structure-from-Motion Multi-View Stereo technique with spatial scan imagery. Independently, underwater bathymetry is retrieved by employing a depth inversion technique with a drone-acquired wave field video. After merging the two digital elevation models into a local coordinate, interpolation is performed for areas where terrain measurement is not feasible, ultimately obtaining a continuous nearshore terrain. We applied the proposed survey technique to Jangsa Beach, South Korea, and verified that detailed terrain characteristics, such as berm, can be measured. The proposed UAV-based survey method has significant efficiency in terms of time, cost, and safety compared to existing methods.