• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.11a
• /
• pp.1181-1184
• /
• 2023

최근 드론은 군사 작전, 물류 운송, 인명 구조 등 다양한 분야에서 활용되고 있으며 관련 산업의 규모는 증가하는 추세이다. 이에 따라, GPS 스푸핑, 조종사 비익명화 등의 드론을 향한 공격 기법들 또한 발달하고 있다. 이런 공격들은 드론에 대한 인증을 도입함으로써 대비할 수 있는 공격들이다. 이에, 학계에서는 강건한 인증을 위해 드론 하드웨어의 고유 특성을 활용할 수 있는 RF 신호, 소리 신호, 드론 내부 센서 신호 등에 기반한 인증 기술들이 연구되어온 바 있다. 본 논문에서는 지금까지의 드론 인증 기술 연구 동향을 분석하고, 이를 기반으로 향후 연구 방향을 제시한다.

• Tunnel and Underground Space
• /
• v.25 no.5
• /
• pp.462-469
• /
• 2015

This study carried out a topographic survey at a small-scale open-pit limestone mine in Korea (the Daesung MDI Seoggyo office) using a popular rotary-wing unmanned aerial vehicle (UAV, Drone, DJI Phantom2 Vision+). 89 sheets of aerial photos could be obtained as a result of performing an automatic flight for 30 minutes under conditions of 100m altitude and 3m/s speed. A total of 34 million cloud points with X, Y, Z-coordinates was extracted from the aerial photos after data processing for correction and matching, then an orthomosaic image and digital surface model with 5m grid spacing could be generated. A comparison of the X, Y, Z-coordinates of 5 ground control points measured by differential global positioning system and those determined by UAV photogrammetry revealed that the root mean squared errors of X, Y, Z-coordinates were around 10cm. Therefore, it is expected that the popular rotary-wing UAV photogrammetry can be effectively utilized in small-scale open-pit mines as a technology that is able to replace or supplement existing topographic surveying equipments.

• Korean Journal of Remote Sensing
• /
• v.36 no.5_1
• /
• pp.667-677
• /
• 2020

Crop water stress can be detected based on soil moisture content, crop physiological characteristics and remote-sensing technology. The detection of crop water stress is an important issue for the accurate assessment of yield decline. The crop water stress index (CWSI) has been introduced based on the difference between leaf and air temperature. In this paper, drone-based thermal infrared image was used to map of crop water stress in water control plot (WCP) and water deficit plot (WDP) over spring chinese cabbage fields. The spatial distribution map of CWSI was in strong agreement with the abnormal growth response factors (plant height, plant diameter, and measured value by chlorophyll meter). From these results, CWSI can be used as a good method for evaluation of crop abnormal growth monitoring.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.10
• /
• pp.633-639
• /
• 2018

High-resolution images using remote sensing (RS) is importance to secure for spatial classification depending on the characteristics of the complex and various factors that make up the river environment. The purpose of this study is to evaluate the accuracy of the classification results and to suggest the possibility of applying the high resolution hyperspectral images obtained by using the drone to perform spatial classification. Hyperspectral images obtained from study area were reduced the dimensionality with PCA and MNF transformation to remove effects of noise. Spatial classification was performed by supervised classifications such as MLC(Maximum Likelihood Classification), SVM(Support Vector Machine) and SAM(Spectral Angle Mapping). In overall, the highest classification accuracy was showed when the MLC supervised classification was used by MNF transformed image. However, it was confirmed that the misclassification was mainly found in the boundary of some classes including water body and the shadowing area. The results of this study can be used as basic data for remote sensing using drone and hyperspectral sensor, and it is expected that it can be applied to a wider range of river environments through the development of additional algorithms.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.36 no.6
• /
• pp.565-572
• /
• 2018

Recently, UAVs (Unmanned Aerial Vehicles) or drones have gained popularity for the engineering surveying and mapping because they enable the rapid data acquisition and processing as well as their operation cost is low. The applicable fields become much wider including the topographic monitoring, agriculture, and forestry. It is reported that the high geospatial accuracy is achievable with the drone photogrammetry for many applications. However most studies reported the best achievable mapping results using well-distributed ground control points though some studies investigated the impact of control points on the accuracy. In this study, we focused on the drone mapping of corridors such as roads and pipelines. The distribution and the number of control points along the corridor were diversified for the accuracy assessment. In addition, the effects of the camera self-calibration and the number of the image strips were also studied. The experimental results showed that the biased distribution of ground control points has more negative impact on the accuracy compared to the density of points. The prior camera calibration was favored than the on-the-fly self-calibration that may produce poor positional accuracy for the case of less or biased control points. In addition, increasing the number of strips along the corridor was not helpful to increase the positional accuracy.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.23 no.4
• /
• pp.234-252
• /
• 2020

This paper is to verify the effectiveness of 'Hybrid Disaster Management Strategy' that integrates 'RTM(Real-time Monitoring) based On-line' and 'UAV based Off-line' system. For landslide prone area where sensors were installed, the conventional way of risk management so far has entirely relied on RTM data collected from the field through the instrumentation devices. But it's not enough due to the limitation of'Pin-point sensor'which tend to provide with only the localized information where sensors have stayed fixed. It lacks, therefore, the whole picture to be grasped. In this paper, utilizing 'Digital Photogrammetry Software Pix4D', the possibility of inference for the deformation of ungauged area has been reviewed. For this purpose, actual measurement data from RTM were compared with the estimated value from 3D point cloud outcome by UAV, and the consequent results has shown very accurate in terms of RMSE.

• Journal of the Korea Society for Simulation
• /
• v.31 no.4
• /
• pp.23-31
• /
• 2022

The development of innovative technology through the 4th Industrial Revolution is actively used in the defense field. In particular, surveillance and reconnaissance capabilities using drones will be of great help to the development of military combat capabilities, such as preparing for future military personnel reductions and reinforcing alert capabilities. In this study, we analyze the combat efficiency of drones how helpful drones can be to the military operations through simulations. Drones and enemy move in the efficient shortest path within a two-dimensional space in which operational areas are mapped into number such as detection probability. Based on the detection probability of an enemy infiltrating along the path with the lowest detection probability, the detection probability change that occurs whenever a drone is additionally deployed is presented, and we analyze the combat efficiency according to the additional drone input. Simulation proves that the increase in combat efficiency decreases as more drones are added in small operational areas such as company-level operational areas. This study is expected to contribute to the efficient operation of a limited number of drones in company-level units and to help determine the most desirable quantity of drones for additional combat power improvement.

• Journal of Aerospace System Engineering
• /
• v.17 no.4
• /
• pp.133-143
• /
• 2023

As drones (also known as UAV) become popular with advanced information and communication technology (ICT), they have been utilized for various fields (agriculture, architecture, and so on). However, malicious attackers with advanced drones may pose a threat to critical national infrastructures. Thus, anti-drone systems have been developed to respond to drone threats. In particular, remote identification data (R-ID)-based UAV detection and identification systems that detect and identify illegal drones with R-ID broadcasted by drones have been developed, and are widely employed worldwide. However, this R-ID-based UAV detection/identification system is vulnerable to security due to wireless broadcast characteristics. In this paper, we analyze the security vulnerabilities of DJI Aeroscope, a representative example of the R-ID-based UAV detection and identification system, and propose a replay-attack-based neutralization method using the analyzed vulnerabilities. To validate the proposed method, it is implemented as a software program, and verified against four types of attacks in real test environments. The results demonstrate that the proposed neutralization method is an effective neutralization method for R-ID-based UAV detection and identification systems.

• Korean Journal of Remote Sensing
• /
• v.39 no.5_2
• /
• pp.743-754
• /
• 2023

The global occurrence of myriad natural disasters and incidents, catalyzed by climate change and extreme meteorological conditions, has engendered substantial human and material losses. International organizations such as the International Charter have established an enduring collaborative framework for real-time coordination to provide high-resolution satellite imagery and geospatial information. These resources are instrumental in the management of large-scale disaster scenarios and the expeditious execution of recovery operations. At the national level, the operational deployment of advanced National Earth Observation Satellites, controlled by National Geographic Information Institute, has not only catalyzed the advancement of geospatial data but has also contributed to the provisioning of damage analysis data for significant domestic and international disaster events. This special edition of the National Disaster Management Research Institute delineates the contemporary landscape of major disaster incidents in the year 2023 and elucidates the strategic blueprint of the government's national disaster safety system reform. Additionally, it encapsulates the most recent research accomplishments in the domains of artificial satellite systems, information and communication technology, and spatial information utilization, which are paramount in the institution's disaster situation management and analysis efforts. Furthermore, the publication encompasses the most recent research findings relevant to data collection, processing, and analysis pertaining to disaster cause and damage extent. These findings are especially pertinent to the institute's on-site investigation initiatives and are informed by cutting-edge technologies, including drone-based mapping and LiDAR observation, as evidenced by a case study involving the 2023 landslide damage resulting from concentrated heavy rainfall.

• Journal of Internet Computing and Services
• /
• v.25 no.1
• /
• pp.147-155
• /
• 2024

Currently, the 4th Industrial Revolution, like other revolutions, is bringing great change and new life to humanity, and in particular, the demand for and use of drones, which can be applied by combining various technologies such as big data, artificial intelligence, and information and communications technology, is increasing. Recently, it has been widely used to carry out dangerous military operations and missions, such as the Russia-Ukraine war and North Korea's reconnaissance against South Korea, and as the demand for and use of drones increases, concerns about the safety and security of drones are growing. Currently, a variety of research is being conducted, such as detection of wireless communication abnormalities and sensor data abnormalities related to drones, but research on real-time detection of threats using radio frequency characteristic data is insufficient. Therefore, in this paper, we conduct a study to determine whether the characteristic data is normal or abnormal signal data by collecting radio frequency signal characteristic data generated while the drone communicates with the ground control system while performing a mission in a HITL(Hardware In The Loop) simulation environment similar to the real environment. proceeded. In addition, we propose an unsupervised learning-based threat detection system and optimal threshold that can detect threat signals in real time while a drone is performing a mission.