• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.2
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• pp.829-842
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• 2018

Unmanned Aerial Vehicle (UAV) is diversely utilized in our lives such as daily hobbies, specialized video image taking and disaster prevention activities. New ways of UAV application have been explored recently such as UAV-based delivery. However, most UAV systems are being utilized in a passive form such as real-time video image monitoring, filmed image ground analysis and storage. For more proactive UAV utilization, there should be higher-performance UAV and large-capacity memory than those presently utilized. Against this backdrop, this study described the general matters on proactive software platform and high-performance UAV hardware for real-time target tracking; implemented research on its design and implementation, and described its implementation method. Moreover, in its established platform, this study measured and analyzed the core-specific CPU consumption.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.42-42
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• 2017

Prediction of rice yield during a growing season would be very helpful to magnify rice yield as it also allows better farm practices to maximize yield with greater profit and lesser costs. UAV imagery based automatic detection of rice can be a relevant solution for early prediction of yield. So, we propose an image processing technique to predict rice yield using low altitude UAV images. We proposed $L^*a^*b^*$ color space based image segmentation algorithm. All images were captured using UAV mounted RGB camera. The proposed algorithm was developed to find out rice grain area from the image background. We took RGB image and applied filter to remove noise and converted RGB image to $L^*a^*b^*$ color space. All color information contain in both $a^*$ and $b^*$ layers and by using k-mean clustering classification of these colors were executed. Variation between two colors can be measured and labelling of pixels was completed by cluster index. Image was finally segmented using color. The proposed method showed that rice grain could be segmented and we can recognize rice grains from the UAV images. We can analyze grain areas and by estimating area and volume we could predict rice yield.

• Journal of Korean Society for Quality Management
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• v.41 no.3
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• pp.405-412
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• 2013

Purpose: In this paper, the improvement of the image quality is investigated. The image quality is degraded by the drift phenomenon of EO/IR (Electro-Optical/Infrared) device on UAV. The drift phenomenon means that the image of EO/IR equipment on UAV(Unmanned Aerial Vehicle) moves to the unintended direction. This phenomenon should be improved for successful flight mission. Methods: To improve the drift phenomenon, the drift compensation method, the combination algorithm of FMC(Forward Motion Compensation) and AMC(Angular Motion Compensation) method, are introduced to calculate pitch and azimuth angle. Result values of pitch and azimuth angle are used for the improvement of image quality in EO/IR control logic. Results: The image quality is quantitatively improved more than 15 times through field test data of flight. Conclusion: Using the drift compensation technique, the image quality for EO/IR equipment is improved over 15 times than existing methods. This means the user of UAV with EO/IR device can perform a successful mission by keeping the line of sight for the target accurately.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.1
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• pp.23-33
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• 2020

Recently, high-resolution images can be easily acquired using UAV (Unmanned Aerial Vehicle), so that it is possible to produce small area observation and spatial information at low cost. In particular, research on the generation of cover maps in crop production areas is being actively conducted for monitoring the agricultural environment. As a result of comparing classification performance by applying RF(Random Forest), SVM(Support Vector Machine) and CNN(Convolutional Neural Network), deep learning classification method has many advantages in image classification. In particular, land cover classification using satellite images has the advantage of accuracy and time of classification using satellite image data set and pre-trained parameters. However, UAV images have different characteristics such as satellite images and spatial resolution, which makes it difficult to apply them. In order to solve this problem, we conducted a study on the application of deep learning algorithms that can be used for analyzing agricultural lands where UAV data sets and small-scale composite cover exist in Korea. In this study, we applied DeepLab V3 +, FC-DenseNet (Fully Convolutional DenseNets) and FRRN-B (Full-Resolution Residual Networks), the semantic image classification of the state-of-art algorithm, to UAV data set. As a result, DeepLab V3 + and FC-DenseNet have an overall accuracy of 97% and a Kappa coefficient of 0.92, which is higher than the conventional classification. The applicability of the cover classification using UAV images of small areas is shown.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.594-596
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• 2022

This paper relates to a system for pre-processing high-definition images acquired through a camera mounted on an unmanned aerial vehicle(UAV) and transmitting them to a server through a mobile communication network. In the case of the existing UAV system for image acquisition service, the acquired image was stored in the external storage device of the camera mounted on the UAV, and the image was checked by directly moving the storage device after the flight was completed. In the case of this method, there is a limitation in that it is impossible to check whether image acquisition or pre-processing is properly performed before directly checking image data through an external storage device. In addition, since the data is stored only in an external storage device, there is a disadvantage that data sharing is cumbersome. In this paper, to solve the above problems, we propose a system that can remotely check images in real time. Furthermore, we propose a system and method capable of performing pre-processing such as geo-tagging and transmission through a mobile communication network in addition to image acquisition through shooting in an UAV.

• 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 Cadastre & Land InformatiX
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• v.48 no.1
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• pp.71-91
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• 2018

In recent years, application of UAV(Unmanned Aerial Vehicle) to seed sowing and pest control has been actively carried out in the field of agriculture. In this study, UAS(Unmanned Aerial System) is constructed by combining image sensor of various wavelength band and SfM((Structure from Motion) based image analysis technique in UAV. Utilization of UAS based vegetation survey was investigated and the applicability of precision farming was examined. For this purposes, a UAS consisting of a combination of a VIS_RGB(Visible Red, Green, and Blue) image sensor, a modified BG_NIR(Blue Green_Near Infrared Red) image sensor, and a TIR(Thermal Infrared Red) sensor with a wide bandwidth of $7.5{\mu}m$ to $13.5{\mu}m$ was constructed for a low cost UAV. In addition, a total of ten vegetation indices were selected to investigate the chlorophyll, nitrogen and water contents of plants with visible, near infrared, and infrared wavelength's image sensors. The images of each wavelength band for the test area were analyzed and the correlation between the distribution of vegetation index and the vegetation index were compared with status of the previously surveyed vegetation and ground cover. The ability to perform vegetation state detection using images obtained by mounting multiple image sensors on low cost UAV was investigated. As the utility of UAS equipped with VIS_RGB, BG_NIR and TIR image sensors on the low cost UAV has proven to be more economical and efficient than previous vegetation survey methods that depend on satellites and aerial images, is expected to be used in areas such as precision agriculture, water and forest research.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.11
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• pp.13-18
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• 2019

The DEM(Digital Elevation Model) is a three-dimensional spatial information that stores the height of the terrain as a numerical value. This means the elevation of the terrain not including the vegetation and the artifacts. The DEM is used in various fields, such as 3D visualization of the terrain, slope, and incense analysis, and calculation of the quantity of construction work. Recently, many studies related to the construction of 3D geospatial information have been conducted, but research related to DEM generation is insufficient. Therefore, in this study, a DEM was constructed using a MMS (Mobile Mapping System), UAV image, and UAV LiDAR (Light Detection And Ranging), and the accuracy evaluation of each result was performed. As a result, the accuracy of the DEM generated by MMS and UAV LiDAR was within ± 4.1cm, and the accuracy of the DEM using the UAV image was ± 8.5cm. The characteristics of MMS, UAV image, and UAV LiDAR are presented through a comparison of data processing and results. The DEM construction using MMS and UAV can be applied to various fields, such as an analysis and visualization of the terrain, collection of basic data for construction work, and service using spatial information. Moreover, the efficiency of the related work can be improved greatly.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.4
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• pp.1057-1071
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• 2016

Privacy-right infringement using unmanned aerial vehicle (UAV) usually occurs due to the unregistered small UAV with the image data processing equipment. In this paper we propose that privacy protection acts, Personal Information Protection Act, Information and Communications Network Act, are complemented to consider the mobility of image data processing equipment installed on UAV. Furthermore, we suggest the regulations for classification of small UAVs causing the biggest concern of privacy-right infringement are included in aviation legislations. In addition, technological countermeasures such as recognition of UAV photographing and masking of identifying information photographed by UAV are proposed.

• 한국항공운항학회:학술대회논문집
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• 2004.11a
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• pp.275-278
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• 2004

In this paper, development of the Image Transmission and Receiving System(ITRS) for UAV is being introduced. ITRS can tranfer the imagery informations from UAV to the multiple sites at the same time. Analog video signal is encoded by using MPEG4 protocol at the Image Transmission System, and the encoded digital video data can be transmitted to the various locations where the Image Receiving System decodes and displays the received video data. The ITRS might be a very efficient method of sharing UAV's information at a low cost.