• 지적과 국토정보
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• 제49권2호
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• pp.169-185
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• 2019

기존의 위성을 활용한 영상과 항공사진은 광활한 지역의 자료를 제공하는 장점이 있지만, 특정 지역의 자료를 원하는 시점에 촬영하여 가공하는 것의 한계와 짧은 주기의 반복적인 촬영이라는 어려운 측면이 있다. 이와 같은 단점을 극복하기 위한 많은 신기술이 개발되면서 지적정보 구축방법은 빠르게 변화하고 있다. 특히 UAV를 이용하여 신속하고 정확하게 지적정보를 구축하는 무인항공측량이 지적정보 취득기술로 관심도가 높아졌다. 따라서 본 연구에서는 UAV를 이용하여 지적측량 대상 지역의 지적측량 업무 적용 방안을 제시하고자 하였다. 이를 위해 관측지역별 제작된 고해상도 영상의 정확도를 비교.분석해 보고, 검증된 영상과 지적 관련 자료를 활용해 기존 지적업무에 적용하고자 하였다. 그리고 지적측량 관련 법률 규정 현황과 UAV의 기술적 특성 등을 분석해 지적측량에 활용 가능성을 분석해 보고 이를 기초로 도입을 위한 기술적, 법제도적 개선 방안을 제시하고자 한다.

• 한국GIS학회:학술대회논문집
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• 한국GIS학회 2008년도 공동추계학술대회
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• pp.245-246
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• 2008

The goal of this study is to explore the possibility of KLT tracker for tracking the features between two images including rotation and shift. As a test site, Jangsu-Gun area of South Korea is selected and the images taken from UAV camera are used for analysis. The analysis was carried out using KLT tracker developed in a PC environment. The results of the experiment used two images with the large overlapping area are compared with the results of two images with the little overlapping area and rotation. Overall, the research indicates that the integrated features of littlerotation and motion images can significantly increase during the tracking process. But using KLT tracker for extracting and tracking features between images with large rotation and motion, the number of tracked features are decreased.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 2017년도 춘계공동학술대회
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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.

• 한국농공학회논문집
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• 제59권3호
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• pp.21-28
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• 2017

The main goal of this paper is to assess application of UAV (Unmanned Aerial Vehicle) remote sensing and GIS based images in detection and measuring of rice field drought area in South Korea. Drought is recurring feature of the climatic events, which often hit South Korea, bringing significant water shortages, local economic losses and adverse social consequences. This paper describes the assesment of the near-realtime drought damage monitoring and reporting system for the agricultural drought region. The system is being developed using drought-related vegetation characteristics, which are derived from UAV remote sensing data. The study area is $3.07km^2$ of Wonbuk-myeon, Taean-gun, Chungnam in South Korea. UAV images were acquired three times from July 4 to October 29, 2015. Three images of the same test site have been analysed by object-based image classification technique. Drought damaged paddy rices reached $754,362m^2$, which is 47.1 %. The NongHyeop Agricultural Damage Insurance accepted agricultural land of 4.6 % ($34,932m^2$). For paddy rices by UAV investigation, the drought monitoring and crop productivity was effective in improving drought assessment method.

• 한국농공학회논문집
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• 제57권6호
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• pp.91-97
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• 2015

The Unmanned Aerial Vehicles (UAVs) have several advantages over conventional RS techniques. They can acquire high-resolution images quickly and repeatedly. And with a comparatively lower flight altitude i.e. 80~400 m, they can obtain good quality images even in cloudy weather. Therefore, they are ideal for acquiring spatial data in cases of small agricultural field with mixed crop, abundant in South Korea. This paper discuss the use of low cost UAV based remote sensing for classifying crops. The study area, Gochang is produced by several crops such as red pepper, radish, Chinese cabbage, rubus coreanus, welsh onion, bean in South Korea. This study acquired images using fixed wing UAV on September 23, 2014. An object-based technique is used for classification of crops. The results showed that scale 250, shape 0.1, color 0.9, compactness 0.5 and smoothness 0.5 were the optimum parameter values in image segmentation. As a result, the kappa coefficient was 0.82 and the overall accuracy of classification was 85.0 %. The result of the present study validate our attempts for crop classification using high resolution UAV image as well as established the possibility of using such remote sensing techniques widely to resolve the difficulty of remote sensing data acquisition in agricultural sector.

• 한국토양비료학회지
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• 제50권5호
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• pp.409-421
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• 2017

Recently Unmanned Aerial Vehicle (UAV) technology offers new opportunities for assessing crop growth condition using UAV imagery. The objective of this study was to select optimal vegetation indices and regression model for estimating of rice growth using UAV images. This study was conducted using a fixed-wing UAV (Model : Ebee) with Cannon S110 and Cannon IXUS camera during farming season in 2016 on the experiment field of National Institute of Crop Science. Before heading stage of rice, there were strong relationships between rice growth parameters (plant height, dry weight and LAI (Leaf Area Index)) and NDVI (Normalized Difference Vegetation Index) using natural exponential function ($R{\geq}0.97$). After heading stage, there were strong relationships between rice dry weight and NDVI, gNDVI (green NDVI), RVI (Ratio Vegetation Index), CI-G (Chlorophyll Index-Green) using quadratic function ($R{\leq}-0.98$). There were no apparent relationships between rice growth parameters and vegetation indices using only Red-Green-Blue band images.

• 대한토목학회논문집
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• 제34권5호
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• pp.1645-1653
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• 2014

대부분의 지형정보획득을 위한 영상에는 RGB, 근적외선, 열영상이 주로 사용된다. 이 멀티밴드영상은 위성이나 유인항공기에 탑재되어 획득되고 있으나 주기해상도, 비용, 공간해상도, 그리고 구름의 영향 등으로 사용자를 만족시키기 어렵다. 자동항법UAV에 적합한 페이로드와 콘트롤러를 개발한다면 원하는 시간과 주기로 고해상도 멀티밴드영상을 획득할 수 있다. 본 연구에서는 멀티밴드 영상획득을 위한 센서와 페이로드의 개발을 통해 저가의 고해상 영상획득시스템을 구축하고 이를 이용하여 geo-referencing data와 함께 RGB, NIR과 열영상을 획득하였다. 획득한 RGB영상으로 정사모자익영상을 제작하여 검사점에 대한 위치정확도를 분석한 결과 수평좌표에서 0.181m, 수직좌표에서 0.203m의 편차를 얻을 수 있었다. 이는 1:1,000~5000수치지도제작과 소규모지역에 대한 원격탐측이 가능한 공간정확도를 만족하므로 페이로드의 활용성을 검증할 수 있었으며 활용이 기대된다.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 2017년도 춘계공동학술대회
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• pp.45-45
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• 2017

On-site monitoring of vegetable growth parameters, such as leaf length, leaf area, and fresh weight, in an agricultural field can provide useful information for farmers to establish farm management strategies suitable for optimum production of vegetables. Unmanned Aerial Vehicles (UAVs) are currently gaining a growing interest for agricultural applications. This study reports on validation testing of previously developed vegetable growth estimation models based on UAV-based RGB images for white radish and Chinese cabbage. Specific objective was to investigate the potential of the UAV-based RGB camera system for effectively quantifying temporal and spatial variability in the growth status of white radish and Chinese cabbage in a field. RGB images were acquired based on an automated flight mission with a multi-rotor UAV equipped with a low-cost RGB camera while automatically tracking on a predefined path. The acquired images were initially geo-located based on the log data of flight information saved into the UAV, and then mosaicked using a commerical image processing software. Otsu threshold-based crop coverage and DSM-based crop height were used as two predictor variables of the previously developed multiple linear regression models to estimate growth parameters of vegetables. The predictive capabilities of the UAV sensing system for estimating the growth parameters of the two vegetables were evaluated quantitatively by comparing to ground truth data. There were highly linear relationships between the actual and estimated leaf lengths, widths, and fresh weights, showing coefficients of determination up to 0.7. However, there were differences in slope between the ground truth and estimated values lower than 0.5, thereby requiring the use of a site-specific normalization method.

• 농업과학연구
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• 제42권3호
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• pp.269-276
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• 2015

Unmanned Aerial Vehicle (UAV) has several advantages over conventional remote sensing techniques. They can acquire high-resolution images quickly and repeatedly. And with a comparatively lower flight altitude, they can obtain good quality images even in cloudy weather. In this paper, we discussed the state-of-the-art of the domestic and international use of UAV in agricultural sector as well as assessed its utilization and applicability for agricultural environment in Korea. Association of robotic, computer vision and geomatic technologies have established a new paradigm of low-altitude aerial remote sensing that has now been receiving attention from researchers all over the world. In a field study, it has been found that use of UAV imagery in an agricultural subsidy program can reduce the farmers' complain and provide objective evidence. UAV high resolution photography can also be helpful in monitoring the disposal zone for animal carcasses. Due to its expeditiousness and accuracy, UAV imagery can be a very useful tool to evaluate the damage in case of an agricultural disaster for both parties insurance companies and the farmers. Also high spatial and temporal resolution in UAV system can increase the prediction accuracy which in turn help to maintain the agricultural supply and demand chain.

• 한국측량학회지
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• 제35권1호
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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.