• Journal of Korean Society for Geospatial Information Science
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• v.16 no.4
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• pp.79-87
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• 2008

There has been a strong demand for low altitude UAV development in rapid mapping not only to acquire high resolution image with much more low cost and weather independent, compared to satellite surveying or traditional aerial surveying, but also to meet many needs of the aerial photogrammetry. Especially, efficient geocoding of UAV imagery is the key issue. Contrary to high UAV potential for civilian applications, the technology development in photogrammetry for example direct georeferencing is in the early stage and it requires further research and additional technical development. In this study, two approaches are supposed for automatic geocoding of UAV still images by simple affine transformation and block adjustment of affine transformation using minimal ground control points and also evaluated the applicability and quality of geometric model compared to geocoded images generated by commercial S/W.

• Tunnel and Underground Space
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• v.26 no.1
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• pp.24-31
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• 2016

Recently, many studies have been conducted to use fixed-wing and rotary-wing unmanned aerial vehicles (UAVs, Drones) for topographic surveying in open-pit mines. Because the fixed-wing and rotary-wing UAVs have different characteristics such as flight height, speed, time and performance of mounted cameras, their results of topographic surveying at a same site need to be compared. This study selected a construction site in Yangsan-si, Gyeongsangnam-do, Korea as a study area and compared the topographic surveying results from a fixed-wing UAV (SenseFly eBee) and a popular rotary-wing UAV (DJI Phantom2 Vision+). As results of data processing for aerial photos taken from eBee and Phantom2 Vision+, orthomosaic images and digital surface models with about 4 cm grid spacing could be generated. Comparisons of the X, Y, Z-coordinates of 7 ground control points measured by differential global positioning system and those determined by eBee and Phantom2 Vision+ revealed that the root mean squared errors of X, Y, Z-coordinates were around 10 cm, respectively.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.3
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• pp.283-290
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• 2016

Even though existing methods for orthophoto production in traditional photogrammetry are effective in large areas, they are inefficient when dealing with change detection of geometric features and image production for short time periods in small areas. In recent years, the UAV (Unmanned Aerial Vehicle), equipped with various sensors, is rapidly developing and has been implemented in various ways throughout the geospatial information field. The data and imagery of specific areas can be quickly acquired by UAVs at low costs and with frequent updates. Furthermore, the redundancy of geospatial information data can be minimized in the UAV-based orthophoto generation. In this paper, the orthophoto and DEM (Digital Elevation Model) are generated using a standard low-end UAV in small sloped areas which have a rather low accuracy compared to flat areas. The RMSE of the check points is σ_H = ±0.12 m on a horizontal plane and σ_V = ±0.09 m on a vertical plane. As a result, the maximum and mean RMSE are in accordance with the working rule agreement for the airborne laser scanning surveying of the NGII (National Geographic Information Institute) on a 1/500 scale digital map. Through this study, we verify the possibilities of the orthophoto generation in small slope areas using general-purpose low specification UAV rather than a high cost surveying UAV.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.6
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• pp.619-627
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• 2016

Cells of a PV (photovoltaic) module can suffer defects due to various causes resulting in a loss of power output. As a malfunctioning cell has a higher temperature than adjacent normal cells, it can be easily detected with a thermal infrared sensor. A conventional method of PV cell inspection is to use a hand-held infrared sensor for visual inspection. The main disadvantages of this method, when applied to a large-scale PV power plant, are that it is time-consuming and costly. This paper presents an algorithm for automatically detecting defective PV panels using images captured with a thermal imaging camera from an UAV (unmanned aerial vehicle). The proposed algorithm uses statistical analysis of thermal intensity (surface temperature) characteristics of each PV module to verify the mean intensity and standard deviation of each panel as parameters for fault diagnosis. One of the characteristics of thermal infrared imaging is that the larger the distance between sensor and target, the lower the measured temperature of the object. Consequently, a global detection rule using the mean intensity of all panels in the fault detection algorithm is not applicable. Therefore, a local detection rule was applied to automatically detect defective panels using the mean intensity and standard deviation range of each panel by array. The performance of the proposed algorithm was tested on three sample images; this verified a detection accuracy of defective panels of 97% or higher. In addition, as the proposed algorithm can adjust the range of threshold values for judging malfunction at the array level, the local detection rule is considered better suited for highly sensitive fault detection compared to a global detection rule. In this study, we used a panel area extraction method that we previously developed; fault detection accuracy would be improved if panel area extraction from images was more precise. Furthermore, the proposed algorithm contributes to the development of a maintenance and repair system for large-scale PV power plants, in combination with a geo-referencing algorithm for accurate determination of panel locations using sensor-based orientation parameters and photogrammetry from ground control points.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.10
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• pp.197-205
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• 2020

Compared to aerial photogrammetry, UAV photogrammetry has advantages in acquiring and utilizing high-resolution images more quickly. The production of 3D models using UAV photogrammetry has become an important issue at a time when the applications of 3D spatial information are proliferating. Therefore, this study assessed the feasibility of utilizing 3D models produced by UAV photogrammetry through quantitative and qualitative analyses. The qualitative analysis was performed in accordance with the LODs (Level of Details) specified in the 3D Land Spatial Information Construction Regulation. The results showed that the features on planes have a high LoD while features with elevation differences have a low LoD due to the occlusion area and parallax. Quantitative analysis was performed using the 3D coordinates obtained from the CPs (Checkpoints) and edges of nearby structures. The mean errors for residuals at CPs were 0.042 m to 0.059 m in the horizontal and 0.050 m to 0.161 m in the vertical coordinates while the mean errors in the structure's edges were 0.068 m and 0.071 m in horizontal and vertical coordinates, respectively. Therefore, this study confirmed the potential of 3D models from UAV photogrammetry for analyzing the digital twin and slope as well as BIM (Building Information Modeling).

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.1
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• pp.11-22
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• 2017

In recent years, coastal dune erosion has accelerated as various structures have been developed around the coastal dunes. A land cover map should be developed to identify the characteristics of sand dunes and to monitor the condition of sand dunes. The Korean Ministry of Environment's land cover maps suffer from problems, such as limited classes, target areas, and durations. Thus, this study conducted experiments using RGB and multispectral images based on UAV (Unmanned Aerial Vehicle) over an approximately one-year cycle to create a land cover map of coastal dunes. RF (Random Forest) classifier was used for the analysis in accordance with the experimental region's characteristics. The pixel- and object-based classification results obtained by using RGB and multispectral cameras were evaluated, respectively. The study results showed that object-based classification using multispectral images had the highest accuracy. Our results suggest that constant monitoring of coastal dunes can be performed effectively.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.6
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• pp.655-662
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• 2010

Recently, disasters are increasing rapidly due to global warming and abnormal weather conditions, and the scale of damage is also getting wider. In this study, the application of UAV is analyzed based on previous study about disaster and analysis of regulations for disaster. Also, this study is proposed application model to prepare, reponse and restoration from natural disaster to make use of the UAV. This UAV is quick and economic for existing technology, and available to various disaster monitoring. UAV application for disaster monitoring is able to support effective management of disaster by real time aerial monitoring for reponse from natural disaster and damage assessment.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.6
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• pp.475-483
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• 2015

This study examined the feasibility of using an automatic lens distortion correction (ALDC) camera as the payload for a photogrammetric unmanned aerial vehicle (UAV) system. First, lens distortion for the interior orientation (IO) parameters was estimated. Although previous studies have largely ignored decentering distortion, this study revealed that more than 50% of the distortion of the ALDC camera was caused by decentering distortion. Second, we compared the accuracy of bundle adjustment for camera calibration using three image types: raw imagery without the ALDC option; imagery corrected using lens profiles; and imagery with the ALDC option. The results of image triangulation, the digital terrain model (DTM), and the orthoimage using the IO parameters for the ALDC camera were similar to or slightly better than the results using self-calibration. These results confirm that the ALDC camera can be used in a photogrammetric UAV system using only self-calibration.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.5
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• pp.351-357
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• 2019

UAV (Unmanned Aerial Vehicle) images can be exploited for rapid forest fire damage assessment by virtue of UAV systems' advantages. In 2019, catastrophic forest fire occurred in Goseong and Sokcho, Korea and burned 1,757 hectares of forests. We visited the town in Goseong where suffered the most severe damage and conducted UAV flights for forest fire damage assessment. In this study, economic and rapid damage assessment method for forest fire has been proposed using UAV systems equipped with only a RGB sensor. First, forest masking was performed using automatic elevation thresholding to extract forest area. Then ExG (Excess Green) vegetation index which can be calculated without near-infrared band was adopted to extract damaged forests. In addition, entropy filtering was applied to ExG for better differentiation between damaged and non-damaged forest. We could confirm that the proposed forest masking can screen out non-forest land covers such as bare soil, agriculture lands, and artificial objects. In addition, entropy filtering enhanced the ExG homogeneity difference between damaged and non-damaged forests. The automatically detected damaged forests of the proposed method showed high accuracy of 87%.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.1
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• pp.29-38
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• 2014

UAV(Unmanned Aerial vehicle) could be effectively applied in a field of land monitoring for analyzing disaster area and mapping, because it can quickly acquire image data at low costs. For this reason, we reviewed the legal system related to mapping, and proposed suggestions for improving in legal system, due to introducing the UAV to Korean land-monitoring through this paper. Also, we evaluated spatial and time accuracy of the digital map, which are generated from UAV images that were taken for occasional map updates and disaster detections. As a result, the mean error is about 10m if only GPS/INS data used, while using GCP(Ground Control Points) it is about 10cm. Therefore, we conclude that the UAV could be effective method in korea land-monitoring field.