• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.32 no.3
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• pp.213-220
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• 2014

This is the study to conduct the topographical analysis using the orthophotographic data from the waypoint flight using the UAV and constructed the system required for the automatic waypoint flight using the multicopter.. The results of the waypoint photographing are as follows. First, result of the waypoint flight over the area of 9.3ha, take time photogrammetry took 40 minutes in total. The multicopter have maintained the certain flight altitude and a constant speed that the accurate photographing was conducted over the waypoint determined by the ground station. Then, the effect of the photogrammetry was checked. Second, attached a digital camera to the multicopter which is lightweight and low in cost compared to the general photogrammetric unmanned airplane and then used it to check its mobility and economy. In addition, the matching of the photo data, and production of DEM and DXF files made it possible to analyze the topography. Third, produced the high resolution orthophoto(2cm) for the inside of the river and found out that the analysis is possible for the changes in vegetation and topography around the river. Fourth, It would be used for the more in-depth research on landscape analysis such as terrain analysis and visibility analysis. This method may be widely used to analyze the various terrains in cities and rivers. It can also be used for the landscape control such as cultural remains and tourist sites as well as the control of the cultural and historical resources such as the visibility analysis for the construction of DSM.

• Korean Journal of Heritage: History & Science
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• v.51 no.2
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• pp.22-37
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• 2018

Unmanned aerial vehicles (UAV) have attracted considerable attention both at home and abroad. The UAV is equipped with a camera that shoots images, which is advantageous for access to areas where archaeological investigations are not possible. Moreover, it is possible to acquire three-dimensional spatial image information by modeling the terrain through aerial photographing, and it is possible to specify the interpretation of the terrain of the survey area. In addition, if we understand the change of the terrain through comparison with past aerial photographs, it will be very helpful to grasp the existence of the ruins. The terrain modeling for searching these remains can be divided into two parts. First, we acquire the aerial photographs of the current terrain using the drone. Then, using image registration and post-processing, we complete the image-joining and terrain-modeling using past aerial photographs. The completed modeled terrain can be used to derive several analytical results. In the present terrain modeling, terrain analysis such as DSM, DTM, and altitude analysis can be performed to roughly grasp the characteristics of the change in the form, quality, and micro-topography. Past terrain modeling of aerial photographs allows us to understand the shape of landforms and micro-topography in wetlands. When verified with actual findings and overlapping data on the modelling of each terrain, it is believed that changes in hill shapes and buried Microform can be identified as helpful when used in low-flying applications. Thus, modeling data using aerial photographs is useful for identifying the reasons for the inability to carry out archaeological surveys, the existence of terrain and ruins in a wide area, and to discuss the preservation process of the ruins. Furthermore, it is possible to provide various themes, such as cadastral maps and land use maps, through comparison of past and present topographical data. However, it is certain that it will function as a new investigation methodology for the exploration of ruins in order to discover archaeological cultural properties.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.4
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• pp.33-39
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• 2006

In this paper, the collision avoidance of Unmanned Aerial Vehicles(UAVs) by applying the Traffic alert and Collision Avoidance System II(TCAS-II) is introduced. The performances of two UAVs whose maximum vertical rates are different each other are compared and analysed by not only converting many TCAS-II commands into an autopilot input but also implementing a computer program based on the Minimum Operational Performance Standards for TCAS-II. As the alternative to a possible Near Mid-Air Collision for UAVs whose maximum vertical rates are low, we have proposed a modified algorithm considering the maximum vertical rate and altitude. The modified algorithm is available on the assumption that a wider surveillance range is provided by a ADS-B system.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1203-1213
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• 2018

In Arctic and Antarctic ocean, remote sensing is the most effective observation for environmental changes due to the inaccessibility of the regions. Even though satellite, UAV (Unmanned Aerial Vehical) are well known remote sensing platforms, and research vessel also used for automatic measurement on the regions, varied environment of Polar regions require time series and wide coverage of data. Especially, in high latitude, apply an optical satellite remote sensing is not easy due to low sun altitude. In this paper, we introduce an operation of hyper-spectrometer (HyperSAS/Satlantic inc.) which is mounted on Ice Breaker Research Vessel ARAON of Korea Polar Research Institute since 2010, to acquire an above water reflectance atomatically through every research cruise on Arctic and Antarctic ocean and transit both regions. In addition to, auxiliary data for the remotely acquired data, in situ water sampling were also obtained. The above water reflectance and in situ water sampling data are continuously acquired since 2010 will contribute to improve an Ocean Color algorithm in the high latitude and help to understand ocean reflectances over from high latitude through low latitude. Preliminary result from above water reflectance showed characteristics of Arctic ocean and Antarctic Ocean and used to develop algorithms for estimating various ocean factors such as chlorophyll and suspended sediment.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1757-1766
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• 2021

The green coverage ratio is the ratio of the land area to green coverage area, and it is used as a practical urban greening index. The green coverage ratio is calculated based on the land cover map, but low spatial resolution and inconsistent production cycle of land cover map make it difficult to calculate the correct green coverage area and analyze the precise green coverage. Therefore, this study proposes a new method to calculate green coverage area using aerial images and deep neural networks. Green coverage ratio can be quickly calculated using manned aerial images acquired by local governments, but precise analysis is difficult because components of image such as acquisition date, resolution, and sensors cannot be selected and modified. This limitation can be supplemented by using an unmanned aerial vehicle that can mount various sensors and acquire high-resolution images due to low-altitude flight. In this study, we proposed a method to calculate green coverage ratio from manned or unmanned aerial images, and experimentally verified the proposed method. Aerial images enable precise analysis by high resolution and relatively constant cycles, and deep learning can automatically detect green coverage area in aerial images. Local governments acquire manned aerial images for various purposes every year and we can utilize them to calculate green coverage ratio quickly. However, acquired manned aerial images may be difficult to accurately analyze because details such as acquisition date, resolution, and sensors cannot be selected. These limitations can be supplemented by using unmanned aerial vehicles that can mount various sensors and acquire high-resolution images due to low-altitude flight. Accordingly, the green coverage ratio was calculated from the two aerial images, and as a result, it could be calculated with high accuracy from all green types. However, the green coverage ratio calculated from manned aerial images had limitations in complex environments. The unmanned aerial images used to compensate for this were able to calculate a high accuracy of green coverage ratio even in complex environments, and more precise green area detection was possible through additional band images. In the future, it is expected that the rust rate can be calculated effectively by using the newly acquired unmanned aerial imagery supplementary to the existing manned aerial imagery.

• Journal of Advanced Navigation Technology
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• v.26 no.5
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• pp.304-311
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• 2022

Although the SEAD(suppression to enemy air defenses) mission is a strategically important task in modern warfare, the high risk of direct exposure to enemy air defense assets forces to use of unmanned aerial vehicles. this paper proposes a path planning algorithm for SEAD mission for an unmanned aerial vehicle and a method for calculating the mission effectiveness on the planned path. Based on the RRT-based path planning algorithm, a low-altitude ingress/egress flight path that can consider the enemy's short-range air defense threat was generated. The Dubins path-based Intercept path planning technique was used to generate a path that is the shortest path while avoiding the enemy's short-range anti-aircraft threat as much as possible. The ingress/intercept/egress paths were connected in order. In addition, mission effectiveness consisting of fuel consumption, the survival probability, the time required to perform the mission, and the target destruction probability was calculated based on the generated path. The proposed techniques were verified through a scenario.