• Economic and Environmental Geology
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• v.48 no.3
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• pp.205-212
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• 2015

We have verified applicability of UAV(Unmanned Aerial Vehicle) photogrammetry to a mining engineering. The test mine is a smectite mine located at Gyeongju city in Gyeongnam province, Koera. 448 photos over area of $600m{\times}380m$ were taken with overlapped manner using Cannon Mark VI equipped to multicopter DJI S1000, which were processed with AgiSoft Photoscan software to generate orthophoto and DEM model of the study area. photogrammetry data with 10 cm resolution were generated using 6 ground control positions, which were exported to the 3D geological modeling software to make a topographic surface object. Monitoring of amount of ore production and landsliding could be done with less than 1 hours photographing as well as low cost. A direct link between UAV photogrammetry and 3D geological modeling technology might increase productivity of a mine due to appling the topographical surface change immediately according to the mining operation.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.3
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• pp.113-127
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• 2015

Recently, there has been a growing interest in UAS(Unmanned Aerial System), and it is required to develop techniques to effectively detect water body from the recorded images in order to implement flood monitoring using UAS. This study used a UAS with RGB and NIR+RG bands to achieve images, and applied supervised classification method to evaluate the accuracy of water body detection. Firstly, the result for accuracy in water body image classification by RGB images showed high Kappa coefficients of 0.791 and 0.783 for the artificial neural network and minimum distance method respectively, and the maximum likelihood method showed the lowest, 0.561. Moreover, in the evaluation of accuracy in water body image classification by NIR+RG images, the magalanobis and minimum distance method showed high values of 0.869 and 0.830 respectively, and in the artificial neural network method, it was very low as 0.779. Especially, RGB band revealed errors to classify trees or grasslands of Songsan amusement park as water body, but NIR+RG presented noticeable improvement in this matter. Therefore, it was concluded that images with NIR+RG band, compared those with RGB band, are more effective for detection of water body when the mahalanobis and minimum distance method were applied.

• Korean Journal of Remote Sensing
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• v.34 no.2_2
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• pp.393-406
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• 2018

Every year in the ocean, various accidents occur frequently and illegal fishing is rampant. Moreover, their size and frequency are also increasing. In order to reduce losses of life or property caused by these, it is necessary to have a means to perform remote monitoring quickly. As an effective platform of such monitoring means, an Unmanned Aerial Vehicle (UAV) is receiving the spotlight. In these situations where marine accidents or illegal fishing occur, main targets of monitoring are ships. In this study, we propose a UAV based ship monitoring system and suggest a method of determining ship positions using UAV multi-sensory data. In the proposed method, firstly, the position and attitude of individual images are determined by using the pre-performed system calibration results and GPS/INS data obtained at the time when images were acquired. In addition, after the ship being detected automatically or semi-automatically from the individual images, the absolute coordinates of the detected ships are determined. The proposed method was applied to actual data measured at 200 m, 350 m, and 500 m altitude, the ship position can be determined with accuracy of 4.068 m, 8.916 m, and 13.734 m, respectively. According to the minimum standard of a hydrographical survey, the ship positioning results of 200 m and 350 m data satisfy grade S and the results of 500 m data do grade 1a, where the accuracy is required for positioning the coastline and topography less significant to navigation order. Therefore, it is expected that the proposed method can be effectively used for various purposes of marine monitoring or surveying.

• Conservation Science in Museum
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• v.24
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• pp.55-74
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• 2020

The Sacred Bell of Great King Seongdeok is required digital precision recording of conservation conditions because of corrosion and partial abrasion of its patterns and inscriptions. Therefore, this study performed digital documentation of the bell using four types of scanning and unmanned aerial vehicle (UAV) photogrammetry technologies, and performed the various shape analyses through image processing. The modeling results of terrestrial laser scanning and UAV photogrammetry were merged and utilized as basic material for monitoring earthquake-induced structural deformation because these techniques can construct mutual spatial relationships between the bell and its tower. Additionally, precision scanning at a resolution four to nine times higher than that of the previous study provided highly valuable information, making it possible to visualize the patterns and inscriptions of the bell. Moreover, they are well-suited as basic data for identifying surface conservation conditions. To actively apply three-dimensional scanning results to the conservation of the original bell, the time and position of any changes in shape need to be established by further scans in the short-term. If no change in shape is detected by short-term monitoring, the monitoring should continue in medium- and long-term intervals.

• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.807-817
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• 2019

Airborne Gamma Ray Spectrometry (AGRS) with its important applications such as gathering radiation information of ground surface, geochemistry measuring of the abundance of Potassium, Thorium and Uranium in outer earth layer, environmental and nuclear site surveillance has a key role in the field of nuclear science and human life. The Broyden-Fletcher-Goldfarb-Shanno (BFGS), with its advanced numerical unconstrained nonlinear optimization in collaboration with Artificial Neural Networks (ANNs) provides a noteworthy opportunity for modern AGRS. In this study a new AGRS system empowered by ANN-BFGS has been proposed and evaluated on available empirical AGRS data. To that effect different architectures of adaptive ANN-BFGS were implemented for a sort of published experimental AGRS outputs. The selected approach among of various training methods, with its low iteration cost and nondiagonal scaling allocation is a new powerful algorithm for AGRS data due to its inherent stochastic properties. Experiments were performed by different architectures and trainings, the selected scheme achieved the smallest number of epochs, the minimum Mean Square Error (MSE) and the maximum performance in compare with different types of optimization strategies and algorithms. The proposed method is capable to be implemented on a cost effective and minimum electronic equipment to present its real-time process, which will let it to be used on board a light Unmanned Aerial Vehicle (UAV). The advanced adaptation properties and models of neural network, the training of stochastic process and its implementation on DSP outstands an affordable, reliable and low cost AGRS design. The main outcome of the study shows this method increases the quality of curvature information of AGRS data while cost of the algorithm is reduced in each iteration so the proposed ANN-BFGS is a trustworthy appropriate model for Gamma-ray data reconstruction and analysis based on advanced novel artificial intelligence systems.

• Journal of the Korea Institute of Building Construction
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• v.19 no.3
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• pp.239-246
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• 2019

According to the survey on the status of aged buildings in Korea, A number of concrete buildings deterioration such as houses and apartment buildings has been increased rapidly. To solve this problem, the research related to the facility management, that is one of the importance factor, for monitoring buildings has been increased. The research is divided into Survey-based and Technique-based. However, the problem is that Survey-based research is required a lot of time, money and manpower for management. Also, safety cannot be guaranteed in the case of high-rise buildings. Technique-based research has limitations to applying to the current facility maintenance system, as detailed information of deteriorated facilities is difficult to grasp and errors in accuracy are feared. Therefore, this paper contribute to improve the environment of facility management by 4D maps using UAV, camera and Pix4D mapper program to make 3D model. In addition, it is expected to suggest that residents will be offered easy verification to their buildings deterioration.

• Nuclear Engineering and Technology
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• v.55 no.8
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• pp.2927-2934
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• 2023

The influence of the atmospheric radon background on the airborne gamma spectrum can seriously affect researchers' judgement of ground radiation information. However, due to load and endurance, unmanned aerial vehicle (UAV)-borne gamma-ray spectrometry is difficulty installing upward-looking detectors to monitor atmospheric radon background. In this paper, an advanced spectral-ratio method was used to correct the atmospheric radon background for a UAV-borne gamma-ray spectrometry in Inner Mongolia, China. By correcting atmospheric radon background, the ratio of the average count rate of U window in the anomalous radon zone (S5) to that in other survey zone decreased from 1.91 to 1.03, and the average uranium content in S5 decreased from 4.65 mg/kg to 3.37 mg/kg. The results show that the advanced spectral-ratio method efficiently eliminated the influence of the atmospheric radon background on the UAV-borne gamma-ray spectrometry to accurately obtain ground radiation information in uranium exploration. It can also be used for uranium tailings monitoring, and environmental radiation background surveys.

• Korean Journal of Remote Sensing
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• v.40 no.2
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• pp.151-166
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• 2024

Coastal debris presents a significant environmental threat globally. This research sought to improve the monitoring methods for coastal debris by employing deep learning and remote sensing technologies. To achieve this, an object detection approach utilizing the You Only Look Once (YOLO)v8 model was implemented to develop a comprehensive image dataset for 11 primary types of coastal debris in our country, proposing a protocol for the real-time detection and analysis of debris. Drone imagery was collected over Sinja Island, situated at the estuary of the Nakdong River, and analyzed using our custom YOLOv8-based analysis program to identify type-specific hotspots of coastal debris. The deployment of these mapping and analysis methodologies is anticipated to be effectively utilized in managing coastal debris.

• Korean Journal of Remote Sensing
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• v.36 no.5_2
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• pp.989-1006
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• 2020

Building change monitoring based on building detection is one of the most important fields in terms of monitoring artificial structures using high-resolution multi-temporal images such as CAS500-1 and 2, which are scheduled to be launched. However, not only the various shapes and sizes of buildings located on the surface of the Earth, but also the shadows or trees around them make it difficult to detect the buildings accurately. Also, a large number of misdetection are caused by relief displacement according to the azimuth and elevation angles of the platform. In this study, object-based building detection was performed using the azimuth angle of the Sun and the corresponding main direction of shadows to improve the results of building change detection. After that, the platform's azimuth and elevation angles were used to detect changed buildings. The object-based segmentation was performed on a high-resolution imagery, and then shadow objects were classified through the shadow intensity, and feature information such as rectangular fit, Gray-Level Co-occurrence Matrix (GLCM) homogeneity and area of each object were calculated for building candidate detection. Then, the final buildings were detected using the direction and distance relationship between the center of building candidate object and its shadow according to the azimuth angle of the Sun. A total of three methods were proposed for the building change detection between building objects detected in each image: simple overlay between objects, comparison of the object sizes according to the elevation angle of the platform, and consideration of direction between objects according to the azimuth angle of the platform. In this study, residential area was selected as study area using high-resolution imagery acquired from KOMPSAT-3 and Unmanned Aerial Vehicle (UAV). Experimental results have shown that F1-scores of building detection results detected using feature information were 0.488 and 0.696 respectively in KOMPSAT-3 image and UAV image, whereas F1-scores of building detection results considering shadows were 0.876 and 0.867, respectively, indicating that the accuracy of building detection method considering shadows is higher. Also among the three proposed building change detection methods, the F1-score of the consideration of direction between objects according to the azimuth angles was the highest at 0.891.

• Journal of The Korean Society of Grassland and Forage Science
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• v.41 no.4
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• pp.295-301
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• 2021

In this study we developed the ground-based multispectral camera system to determine proper period to build and apply the calibration equation for dry matter of winter forage corps monitoring by unmanned aerial vehicle (UAV). Normalized difference vegetation index (NDVI) of rye, whole barley and Italian ryegrass (IRG) were measured and the growth period was divide by NDVI increasing period and decreasing period. Day of the maximum NDVI value of rye, whole barley and IRG were 8th, 9th and 5th April 2020. Regression analysis showed that the correlation coefficients (R²) between dry matter and NDVI were 0.84, 0.84, 0.78 during NDVI increasing period and 0.00, 0.02, 0.27 during NDVI decreasing period. Therefore, detailed NDVI monitoring is required to determine the proper period to build and apply the calibration equation and the ground-based multispectral camera system was effective tool for detailed NDVI monitoring.