• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.3
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• pp.117-126
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• 2018

Due to intelligent transport systems, location-based applications, and augmented reality, demand for image maps and 3D (Three-Dimensional) maps is increasing. As a result, data acquisition using UAV (Unmanned Aerial Vehicles) has flourished in recent years. However, even though orthophoto map production and research using UAVs are flourishing, few studies on 3D modeling have been conducted. In this study, orthophoto and 3D modeling research was performed using various angle images acquired by a UAV. For orthophotos, accuracy was evaluated using a GPS (Global Positioning System) survey that employed VRS (Virtual Reference Station) acquired checkpoints. 3D modeling was evaluated by calculating the RMSE (Root Mean Square Error) of the difference between the outline height values of buildings obtained from the GPS survey to the corresponding 3D modeling height values. The orthophotos satisfied the acceptable accuracy of NGII (National Geographic Information Institute) for a 1/500 scale map from all angles. In the case of 3D modeling, models based on images taken at 45 degrees revealed better accuracy of building outlines than models based on images taken at 30, 60, or 75 degrees. To summarize, it was shown that for orthophotos, the accuracy for 1/500 maps was satisfied at all angles; for 3D modeling, images taken at 45 degrees produced the most accurate models.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.3
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• pp.153-164
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• 2018

Calculating the relevant length of left turn storages in urban intersections is very crucial in road designs. A left turn lane consists of deceleration lanes and left turn storages. In this study, we developed methods for calculating relevant lengths of left turn storages that vary at each intersection using UAV (Unmanned Aerial Vehicle) spatial images. Problems of conventional design techniques are applying the same number of left turn vehicles (N) using Poisson distribution without considering land use types, using a vehicle length that may not be measurable when applying the length of waiting vehicles (S), and using same storage length coefficient (${\alpha}$), 1.5, for every intersections. In order to solve these problems, we estimated the number of left turn vehicles (N) using an empirical distribution, suggested to use headways of vehicles for (S) to calculate the length of waiting vehicles (S) with a help of using UAV spatial images, and defined ranges of storage length coefficient (${\alpha}$) from 1.0 to 1.5 for flexible design. For more convenient design, it is suitable to classify two cases when possible to know and impossible to know about ratio of large trucks among vehicles when planning an intersection. We developed formula for each case to calculate left turn storage lengths of a minimum and a maximum. By applying developed methods and values, more efficient signalized intersection operation can be accomplished.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.433-442
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• 2018

The reservoir area is defined as the area surrounded by the planned flood level of the dam or the land under the planned flood level of the dam. In this study, supervised classification based on RF (Random Forest), which is a representative machine learning technique, was performed to detect cropland in the reservoir area. In order to classify the cropland in the reservoir area efficiently, the GLCM (Gray Level Co-occurrence Matrix), which is a representative technique to quantify texture information, NDWI (Normalized Difference Water Index) and NDVI (Normalized Difference Vegetation Index) were utilized as additional features during classification process. In particular, we analyzed the effect of texture information according to window size for generating GLCM, and suggested a methodology for detecting croplands in the reservoir area. In the experimental result, the classification result showed that cropland in the reservoir area could be detected by the multispectral, NDVI, NDWI and GLCM images of UAV, efficiently. Especially, the window size of GLCM was an important parameter to increase the classification accuracy.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.6
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• pp.645-653
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• 2020

Recently studies on how to use the UAV (Unmanned Aerial Vehicle) are actively being conducted, and the National Geographic Information Institute published the 『Work Guidelines for Public Surveying of Unmanned Aerial Vehicles』. However, the guidelines do not provide the optimum shooting conditions required for each application. In this study, we tried to find the suitable shooting conditions for the production of 3D (Three-dimensional) spatial information and orthophoto. To this end, 45 experiments were conducted by various altitudes, overlaps, and camera angles within an above ground level of 150m. For evaluating the 3D modeling by shooting conditions, point densities of 9 verification areas were analyzed, and to evaluate the orthophotos, 1/1,000 digital maps were compared. Considering the quality of the output and the processing time for precise 3D construction, an altitude of 50m, an overlap of 70~80%, and a camera angle of 80~90° are suitable as shooting conditions, and an altitude of 100m and camera angle of 80~90° are suitable for orthophoto generation.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.525-533
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• 2019

There are two feature collection methods in digital mapping using the UAV (Unmanned Aerial Vehicle) Photogrammetry: vectorization and stereo plotting. In vectorization, planar information is extracted from orthomosaics and elevation value obtained from a DSM (Digital Surface Model) or a DEM (Digital Elevation Model). However, the exact determination of the positional accuracy of 3D features such as ground facilities and buildings is very ambiguous, because the accuracy of vectorizing results has been mainly analyzed using only check points placed on the ground. Thus, this study aims to review the possibility of 3D spatial information acquisition and digital map production of vectorization by analyzing the corner point coordinates of different layers as well as check points. To this end, images were taken by a Phantom 4 (DJI) with 3.6 cm of GSD (Ground Sample Distance) at altitude of 90 m. The outcomes indicate that the horizontal RMSE (Root Mean Square Error) of vectorization method is 0.045 cm, which was calculated from residuals at check point compared with those of the field survey results. It is therefore possible to produce a digital topographic (plane) map of 1:1,000 scale using ortho images. On the other hand, the three-dimensional accuracy of vectorization was 0.068~0.162 m in horizontal and 0.090~1.840 m in vertical RMSE. It is thus difficult to obtain 3D spatial information and 1:1,000 digital map production by using vectorization due to a large error in elevation.

• Korean Journal of Remote Sensing
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• v.32 no.6
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• pp.647-656
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• 2016

Growth of UAV technology leads to expansion of UAV image applications. Many UAV image-based applications use a method called incremental bundle adjustment. However, incremental bundle adjustment produces large computation overhead because it attempts feature matching from all image pairs. For efficient feature matching process we have to confine matching only for overlapping pairs using exterior orientation parameters. When exterior orientation parameters are not available, we cannot determine overlapping pairs. We need another methods for feature matching candidate constitution. In this paper we compare matching candidate constitution methods without exterior orientation parameters, including partial feature matching, Bag-of-keypoints, image intensity method. We use the overlapping pair determination method based on exterior orientation parameter as reference. Experiment results showed the partial feature matching method in the one with best efficiency.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.499-506
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• 2019

NDSM (Normalized Digital Surface Model) is key information for the detailed analysis of remote sensing data. Although NDSM can be simply obtained by subtracting a DTM (Digital Terrain Model) from a DSM (Digital Surface Model), in case of UAV (Unmanned Aerial Vehicle) data, it is difficult to get an accurate DTM due to high resolution characteristics of UAV data containing a large number of complex objects on the ground such as vegetation and urban structures. In this study, RGB-based UAV vegetation index, ExG (Excess Green) was used to extract initial seed points having low ExG values for region growing such that a DTM can be generated cost-effectively based on high resolution UAV data. For this process, local window analysis was applied to resolve the problem of erroneous seed point extraction from local low ExG points. Using the DSM values of seed points, region growing was applied to merge neighboring terrain pixels. Slope criteria were adopted for the region growing process and the seed points were determined as terrain points in case the size of segments is larger than 0.25 ㎡. Various slope criteria were tested to derive the optimized value for UAV data-based NDSM generation. Finally, the extracted terrain points were evaluated and interpolation was performed using the terrain points to generate an NDSM. The proposed method was applied to agricultural area in order to extract the above ground heights of crops and check feasibility of agricultural monitoring.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.1
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• pp.31-38
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• 2012

The geo-referencing accuracy of the images acquired by a UAV based multi-sensor system is affected by the accuracy of the mounting parameters involving the relationship between a camera and a GPS/INS system as well as the performance of a GPS/INS system. Therefore, the estimation of the accurate mounting parameters of a multi-sensor system is important. Currently, we are developing a low altitude multi-sensor system based on a UAV, which can monitor target areas in real time for rapid responses for emergency situations such as natural disasters and accidents. In this study, we suggest a system calibration method for the estimation of the mounting parameters of a multi-sensor system like our system. We also generate simulation data with the sensor specifications of our system, and derive an effective flight configuration and the number of ground control points for accurate and efficient system calibration by applying the proposed method to the simulated data. The experimental results indicate that the proposed method can estimate accurate mounting parameters using over five ground control points and flight configuration composed of six strips. In the near future, we plan to estimate mounting parameters of our system using the proposed method and evaluate the geo-referencing accuracy of the acquired sensory data.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.1
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• pp.9-18
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• 2019

The intensity and the number of days of high temperature occurrence are also high and record heat occurred. In addition, the global warming phenomenon is intensifying globally, and especially in South Korea, the urban heat island phenomenon is also occurring due to rapid urbanization due to rapid industrial development. As the temperature of the city rises, it causes problems such as the comfort of the residential living and the cooling load. In this study, the cool roof performance is evaluated according to the roof color to reduce these problems. Unlike previous studies, UAV(Unmanned Aerial Vehicle) thermal infrared camera was used to obtain the surface temperature (white, grey, green, blue, brown, black) according to the rooftop color by remote sensing technique. As a result, the surface temperature of white color was $11{\sim}20^{\circ}C$ lower than other colors. Also air conditioning temperature of white color was $1.5{\sim}4.4^{\circ}C$ lower than other colors and the digital thermometer of white color was about $1.5{\sim}3.5^{\circ}C$ lower than other colors. It was confirmed that the white cool roof performance is the best, and the UAV and the thermal infrared camera can confirm the cool roof performa.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.941-951
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• 2018

Utilization and demand of UAV (unmanned aerial vehicle) for the generation of 3D city model are increasing. In this study, we performed an experiment to adjustment position/orientation of UAV with incomplete attitude information and to extract point cloud data. In order to correct the attitude of the UAV, the rotation angle was calculated by using the continuous position information of UAV movements. Based on this, the corrected position/orientation information was obtained by applying IBA (Incremental Bundle Adjustment) based on photogrammetry. Each pair was transformed into an epipolar image, and the MDR (Multi-Dimensional Relaxation) technique was applied to obtain high precision DSM. Each extracted pair is aggregated and output in the form of a single point cloud or DSM. Using the DJI inspire1 and Phantom4 images, we can confirm that the point cloud can be extracted which expresses the railing of the building clearly. In the future, research will be conducted on improving the matching performance and establishing sensor models of oblique images. After that, we will continue the image processing technology for the generation of the 3D city model through the study of the extraction of 3D cloud It should be developed.