• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.3
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• pp.217-226
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• 2022

In the view of the application of high-precision spatial information production, UAV (Umanned Aerial Vehicle)-Photogrammetry has a problem in that it lacks specific procedures and detailed regulations for quantitative quality verification methods or certification of captured images. In addition, test tools for UAV image quality assessment use only the GSD (Ground Sample Distance), not MTF (Modulation Transfer Function), which reflects image resolution and contrast at the same time. This fact makes often the quality of UAV image inferior to that of manned aerial image. We performed MTF and GSD analysis simultaneously using a siemens star to confirm the necessity of MTF analysis in UAV image quality assessment. The analyzing results of UAV images taken with different payload and sensors show that there is a big difference in σ_MTF values, representing image resolution and the degree of contrast, but slightly different in GSD. It concluded that the MTF analysis is a more objective and reliable analysis method than just the GSD analysis method, and high-quality drone images can only be obtained when the operator make images after judging the proper selection the sensor performance, image overlaps, and payload type. However, the results of this study are derived from analyzing only images acquired by limited sensors and imaging conditions. It is therefore expected that more objective and reliable results will be obtained if continuous research is conducted by accumulating various experimental data in related fields in the future.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1573-1587
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• 2021

A Drone image is an ultra-high-resolution image that is several or tens of times higher in spatial resolution than a satellite or aerial image. Therefore, drone image-based remote sensing is different from traditional remote sensing in terms of the level of object to be extracted from the image and the amount of data to be processed. In addition, the optimal scale and size of data used for model training is different depending on the characteristics of the applied deep learning model. However, moststudies do not consider the size of the object to be found in the image, the spatial resolution of the image that reflects the scale, and in many cases, the data specification used in the model is applied as it is before. In this study, the effect ofspatial resolution and image size of drone image on the accuracy and training time of the semantic segmentation deep learning model of six wintering vegetables was quantitatively analyzed through experiments. As a result of the experiment, it was found that the average accuracy of dividing six wintering vegetablesincreases asthe spatial resolution increases, but the increase rate and convergence section are different for each crop, and there is a big difference in accuracy and time depending on the size of the image at the same resolution. In particular, it wasfound that the optimal resolution and image size were different from each crop. The research results can be utilized as data for getting the efficiency of drone images acquisition and production of training data when developing a winter vegetable segmentation model using drone images.

• Journal of Korean Society for Geospatial Information Science
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• v.10 no.1 s.19
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• pp.19-26
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• 2002

To provide 3D GSIS data on the internet, 3D data structures need to be researched and applied for spatial analysis for subsurface modeling. As for GSIS software R&D trend the following things have pointed out : 3-dimensional geo-processing technologies, internet-based application system development, distributed processing technologies for large volume of spatial information, real-time geo-data processing methodologies, Among them research scope within Internet-based application system or Web-based GSIS generally contains core parts of software development such as Internet application, large volume of spatial database handling, real-time spatial data processing, spatial data transfer and transformation, and volumetric display of processing results. This study shows the method of providing 3D GSIS on the internet using VRML model, which are made of DEM data, draped aerial photo, and VRML script programming. And it is also studied that offering 3D GSIS engine on the internet and precise texture mapping using satellite image and aerial photos.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.1D
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• pp.81-88
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• 2012

This present research was carried out by dividing two parts; field surveying and data processing, in order to analyze changed patterns of a shoreline. Firstly, the shoreline information measured by the precise GPS positioning during long duration was collected. Secondly, the algorithm for detecting an auto boundary with regards to the changed shoreline with multi-image data was developed. Then, a comparative research was conducted. Haeundae beach which is one of the most famous ones in Korea was selected as a test site. RTK-GPS surveying had been performed overall eight times from September 2005 to September 2009. The filed test by aerial Lidar was conducted twice on December 2006 and March 2009 respectively. As a result estimated from both sensors, there is a slight difference. The average length of shoreline analyzed by RTK-GPS is approximately 1,364.6 m, while one from aerial Lidar is about 1,402.5 m. In this investigation, the specific algorithm for detecting the shoreline detection was developed by Visual C++ MFC (Microsoft Foundation Class). The analysis result estimated by aerial photo and satellite image was 1,391.0 m. The level of reliability was 98.1% for auto boundary detection when it compared with real surveying data.

• Journal of the Korea Safety Management & Science
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• v.9 no.5
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• pp.97-101
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• 2007

As the use of vehicle route application and LBS(location based service) are fast grew, the importance of maintaining road network data is also increased. To maintain road data accuracy, we can collect road data by driving real roads with probe vehicle, or using digital image processing for the extraction of roads from aerial imagery. After compare the new road data to current database, we can update the road database, but that job is mostly time and money consuming or can be inaccurate. In this paper, an updating method of using GPS(global positioning system) enabled cell phone is proposed. By using GPS phone, we can update road database easily and sufficiently accurately.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.6
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• pp.401-410
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• 2020

In this study, a real-time Tactical UAS position compensation system based on image information developed to compensate for the weakness of location navigation information during GPS signal interference and jamming / spoofing attack is described. The Tactical UAS (KUS-FT) is capable of automatic flight by switching the mode from GPS/INS integrated navigation to DR/AHRS when GPS signal is lost. However, in the case of location navigation, errors accumulate over time due to dead reckoning (DR) using airspeed and azimuth which causes problems such as UAS positioning and data link antenna tracking. To minimize the accumulation of position error, based on the target data of specific region through image sensor, we developed a system that calculates the position using the UAS attitude, EO/IR (Electric Optic/Infra-Red) azimuth and elevation and numerical map data and corrects the calculated position in real-time. In addition, function and performance of the image information based real-time UAS position compensation system has been verified by ground test using GPS simulator and flight test in DR mode.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.1
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• pp.43-48
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• 2020

Open pit mines for limestone mining require rapid development of technologies and efforts to prevent safety accidents due to rapid deterioration of the slope due to deforestation and rapid changes in the topography. Accurate three-dimensional spatial information on the terrain should be the basis for reducing environmental degradation and safe development of open pit mines. Therefore, this study constructed spatial information about open pit mine using UAV(Unmanned Aerial Vehicle) and analyzed its utility. images and 3D laser scan data were acquired using UAV, and digital surface model, digital elevation model and ortho image were generated through data processing. DSM(Digital Surface Model) and ortho image were constructed using image obtained from UAV. Trees were removed using 3D laser scan data and numerical elevation models were produced. As a result of the accuracy analysis compared with the check points, the accuracy of the digital surface model and the digital elevation model was about 11cm and 8cm, respectively. The use of three-dimensional geospatial information in the mineral resource development field will greatly contribute to effective mine management and prevention of safety accidents.

• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1053-1065
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• 2019

Land use statistics calculation is very informative data as the activity data for calculating exact carbon absorption and emission in post-2020. To effective interpretation by land use category, This study classify automatically image interpretation by land use category applying forest aerial photography (FAP) to deep learning model and calculate national unit statistics. Dataset (DS) applied deep learning is divided into training dataset (training DS) and test dataset (test DS) by extracting image of FAP based national forest resource inventory permanent sample plot location. Training DS give label to image by definition of land use category and learn and verify deep learning model. When verified deep learning model, training accuracy of model is highest at epoch 1,500 with about 89%. As a result of applying the trained deep learning model to test DS, interpretation classification accuracy of image label was about 90%. When the estimating area of classification by category using sampling method and compare to national statistics, consistency also very high, so it judged that it is enough to be used for activity data of national GHG (Greenhouse Gas) inventory report of LULUCF sector in the future.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.1
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• pp.1-10
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• 2017

An UAV (Unmanned Aerial Vehicle) is a flight system that is designed to conduct missions without a pilot. Compared to traditional airborne-based photogrammetry, UAV-based photogrammetry is inexpensive and can obtain high-spatial resolution data quickly. In this study, we aimed to classify the land cover using high-spatial resolution images obtained using a UAV. An RGB camera was used to obtain high-spatial resolution orthoimage. For accurate classification, multispectral image about same areas were obtained using a multispectral sensor. A DSM (Digital Surface Model) and a modified NDVI (Normalized Difference Vegetation Index) were generated using images obtained using the RGB camera and multispectral sensor. Pixel-based classification was performed for twelve classes by using the RF (Random Forest) method. The classification accuracy was evaluated based on the error matrix, and it was confirmed that the proposed method effectively classified the area compared to supervised classification using only the RGB image.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.997-999
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• 2003

The effects of JPEG2000 compression on automated DSM extraction by using the area-based matching are evaluated in this paper. The influences on DSM heights obtained via PCI Geomatics OrthoEngine module are investigated using a single stereo model of 1:5,000 scale aerial photography at image resolution of 20 ${\mu}$m. The experiment design of elevation errors are computed for a range of compression rates from 2:1 to about 100:1, and the DSM which generated from uncompressed image is used as ‘ground truth’ data for comparison. The experimental results show that the standard deviation ranged from 0.9m to 2.5m with the compression ratio from 2 to 100. It is also observed that there is no significant degradation on DSM accuracy up to the compression ratio of 33.