• Journal of Korean Society for Geospatial Information Science
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• v.14 no.1 s.35
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• pp.37-47
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• 2006

In the last years a low-altitude image acquisition technology has been developed in application of frequent change monitoring in urban area md speedy surveillance in disaster area. A low-altitude photogrammetric system have advantages of accurate observation and free data-acquisition time. Especially, an unmaned RC-helicopter, improving safety, durability and portability, comes into the spotlight as a built-in vehicle in close range photogrammetric application due to their capability of safe near-by observation and effective flight performance. This paper gives a methodology for generating image map by development of low cost and timesaving low-altitude photogrammetric UAV(unmaned aerial vehicles) for collecting high-resolution image data, and implement of geo-rectification and image mosaicking.

• 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 the Korean Society of Industry Convergence
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• v.23 no.2_2
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• pp.209-218
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• 2020

Various studies and business investments have been performed on UAV in the field of spatial information industry, and it is judged that this industry has being evolved into an expansion stage as a legalization progresses. In addition, public institutions such as Korea Land and Geospatial Information Corporation, Korea Expressway Corporation, and Korea Land and Housing Corporation which have relatively much utilized spatial information work have entered into the stage of settling with active introduction for reasons of work efficiency and business management. However, surveying drones are still classified as expensive equipment, which is a burden on general business application and technology popularization. Moreover, the stabilization of reliability of various location information acquired from UAV is a part of ongoing research and supplementation. Therefore, in this study, to use image information acquired from low-cost UAV as reliable spatial information data, the flight altitude was changed and compared with the result of double transverse flight with conventional photographing technique. As a result, there was no change in the result value at low altitude, but the result showed more than 30% accuracy and accuracy improvement for the X, Y value at the altitude of 130m or higher than the conventional method.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2010.04a
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• pp.249-250
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• 2010

The study aims to propose a method that shall rapidly acquire 3D spatial information of the frequently changing city areas by using the low altitude aerial images taken by the UAV. The artificial 3D model of the artificial structures was constructed using the aerial image data photographed at the test area, calibration data of the non-metric camera and the results of the ground control point survey. Also, the digital surface model was created for areas that were changed due to a number of civil works. Through the above studies, the possibilities of constructing a 3D virtual city model, renewal of 3D GIS database, abstraction of changed information in geographic features and on-demand updating of the digital map were suggested.

• International Journal of Advanced Culture Technology
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• v.7 no.4
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• pp.327-333
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• 2019

Wind speed data constitute important weather information for aircrafts flying at low altitudes, such as drones. Currently, the accuracy of low altitude wind predictions is much lower than that of high-altitude wind predictions. Deep neural networks are proposed in this study as a method to improve wind speed forecast information. Deep neural networks mimic the learning process of the interactions among neurons in the brain, and it is used in various fields, such as recognition of image, sound, and texts, image and natural language processing, and pattern recognition in time-series. In this study, the deep neural network model is constructed using the wind prediction values generated by the numerical model as an input to improve the wind speed forecasts. Using the ground wind speed forecast data collected at the Boseong Meteorological Observation Tower, wind speed forecast values obtained by the numerical model are compared with those obtained by the model proposed in this study for the verification of the validity and compatibility of the proposed model.

• International Journal of Contents
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• v.13 no.3
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• pp.1-8
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• 2017

Rice plant numbers and density are key factors for yield and quality of rice grains. Precise and properly estimated rice plant numbers and density can assure high yield from rice fields. The main objective of this study was to automatically detect and count rice plants using images of usual field condition from an unmanned aerial vehicle (UAV). We proposed an automatic image processing method based on morphological operation and boundaries of the connected component to count rice plant numbers after transplanting. We converted RGB images to binary images and applied adaptive median filter to remove distortion and noises. Then we applied a morphological operation to the binary image and draw boundaries to the connected component to count rice plants using those images. The result reveals the algorithm can conduct a performance of 89% by the F-measure, corresponding to a Precision of 87% and a Recall of 91%. The best fit image gives a performance of 93% by the F-measure, corresponding to a Precision of 91% and a Recall of 96%. Comparison between the numbers of rice plants detected and counted by the naked eye and the numbers of rice plants found by the proposed method provided viable and acceptable results. The $R^2$ value was approximately 0.893.

• Korean Journal of Remote Sensing
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• v.36 no.2_2
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• pp.337-349
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• 2020

In this study, Helikite, Low Altitude Remote Sensing (LARS) platform, was used to acquire coastal images. In the obtained image, the land and water masses were divided using four types of region clustering algorithms, and then waterline was extracted using edge detection. Quantitative comparisons were not possible due to the lack of in-situ waterline data. But, based on the image of the infrared band where water masses and land are relatively clear, the waterlines extracted by each algorithm were compared. As a result, it was found that each algorithm differed significantly in the part where the distinction between water masses and land was ambiguous. This is considered to be a difference in the process of selecting the threshold value of the digital number that each algorithm uses to distinguish the regions. The extraction of waterlines through various algorithms is expected to be used in conjunction with a Low Altitude Remote Sensing system that can be continuously monitored in the future to explain the rapid changes in coastal shape through several years of long-term data from fixed areas.

• Journal of Korean Society for Geospatial Information Science
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• v.16 no.4
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• pp.79-87
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• 2008

There has been a strong demand for low altitude UAV development in rapid mapping not only to acquire high resolution image with much more low cost and weather independent, compared to satellite surveying or traditional aerial surveying, but also to meet many needs of the aerial photogrammetry. Especially, efficient geocoding of UAV imagery is the key issue. Contrary to high UAV potential for civilian applications, the technology development in photogrammetry for example direct georeferencing is in the early stage and it requires further research and additional technical development. In this study, two approaches are supposed for automatic geocoding of UAV still images by simple affine transformation and block adjustment of affine transformation using minimal ground control points and also evaluated the applicability and quality of geometric model compared to geocoded images generated by commercial S/W.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.5
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• pp.775-784
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• 2023

Drone images are being used more and more actively in the fields of surveying and spatial information, and are rapidly replacing existing aerial and satellite images. The technology of quickly acquiring real-time data at low cost and processing it is now being applied to actual industries beyond research. However, there are also problems encountered as this progresses. When high-resolution spatial information is acquired using a general 2D flight plan for a terrain with sever undulations, problems arise due to the difference in resolution of the data. In particular, when a low-altitude high-resolution image is taken using a drone in a mountainous or steep terrain, there may be a problem in image matching due to a resolution difference caused by terrain undulations. This problem occurs because a drone acquires data while flying on a 2D plane at a fixed altitude, just like conventional aerial photography. In order to acquire high-quality 3D data using a drone, the scale difference for the shooting distance should be considered. In addition, in order to obtain facade images of large structures, it is necessary to take images in 3D space. In this study, in order to improve the disadvantages of the 2D flight method, a 3D flight plan was established for the study area, and it was confirmed that high-quality 3D spatial information could be obtained in this way.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.23 no.5
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• pp.59-71
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• 2020

Using high-resolution stereoscopic aerial images (in 2008, 2012 and 2016), we conducted to analyze the spatial characteristics affecting evergreen coniferous die-off in the northwestern ridge (major distribution area such as Abies nephrolepis), Seoraksan National Park. The detected number of dead trees at evergreen coniferous forest (5.24㎢) was 1,223 in 2008, was 2,585 in 2012 and was 3,239 in 2016. The number of cumulated dead trees was 7,047 in 2016. In recent years, the number of dead trees increased relatively in the northwest ridge, Seoraksan National Park. Among the analysed spatial factor (altitude, aspect, slope, solar radiation and topographic wetness index), the number of dead trees was increased in the conditions with high altitude, steep slope and dry soil moisture. A spatial distribution of dead tree was divided into 2 groups largely (high altitude with high solar radiation, low altitude with steep slope). In conclusion, the dead trees of evergreen coniferous were concentrated at spatial distribution characteristics causing dryness in the northwestern ridge, Seoraksan National Park.