• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.104-105
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• 2023

해양 관측과 위성 원격탐사를 이용하여 시공간적으로 다양하게 변하는 생태 어장 환경 및 선박 관련 자료를 획득할 수 있다. 이번 연구의 주요 목적은 야간 불빛 위성 자료를 이용하여 광범위한 해역에 대한 어선의 위치 분포를 파악하는 딥러닝 기반 모델을 제안하는 것이다. 제안한 모델의 정확성을 평가하기 위해 야간 조업 어선의 위치를 포함하고 있는 AIS(Automatic Identification System) 정보와 상호 비교 평가 하였다. 이를 위해, 먼저 AIS 자료를 획득 및 분석하는 방법을 소개한다. 해양안전종합시스템(General Information Center on Maritime Safety & Security, GICOMS)으로부터 제공받은 AIS 자료는 동적정보와 정적정보로 나뉜다. 동적 정보는 일별 자료로 구분되어있으며, 이 정보에는 해상이동업무식별번호(Maritime Mobile Service Identity, MMSI), 선박의 시간, 위도, 경도, 속력(Speed over Ground, SOG), 실침로(Course over Ground, COG), 선수방향(Heading) 등이 포함되어 있다. 정적정보는 1개의 파일로 구성되어 있으며, 선박명, 선종 코드, IMO Number, 호출부호, 제원(DimA, DimB, DimC, Dim D), 홀수, 추정 톤수 등이 포함되어 있다. 이번 연구에서는 선박의 정보에서 어선의 정보를 추출하여 비교 자료로 사용하였으며, 위성 자료는 구름의 영향이 없는 깨끗한 날짜의 영상 자료를 선별하여 사용하였다. 야간 불빛 위성 자료, 구름 정보 등을 이용하여 야간 조업 어선의 불빛을 감지하는 심층신경망(Deep Neural Network; DNN) 기반 모델을 제안하였다. 본 연구의결과는 야간 어선의 분포를 감시하고 한반도 인근 어장을 보호하는데 기여할 것으로 기대된다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.4
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• pp.615-627
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• 2024

With the evolution of power systems and advancements in IT technology, there is an increasing demand to shift from serial-based communication to TCP/IP-based communication. However, TCP/IP communication entails various security threats, necessitating extensive consideration from an information security perspective. Security measures such as authentication and encryption cannot be rapidly implemented due to issues like the replacement of Remote Terminal Units (RTUs) and the performance requirements of encryption algorithms. This paper proposes a state estimation-based intrusion detection model to identify and effectively detect threats to power control systems in such a context. The proposed model, in addition to signature detection methods, verifies the validity of acquired data, enabling it to detect attacks that are difficult to identify using traditional methods, such as data tampering.

• Journal of Korea Water Resources Association
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• v.53 no.10
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• pp.845-859
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• 2020

Climate change and recent heat waves have drawn public attention toward other environmental issues, such as water pollution in the form of algal blooms, chemical leaks, and oil spills. Water pollution by the leakage of chemicals may severely affect human health as well as contaminate the air, water, and soil and cause discoloration or death of crops that come in contact with these chemicals. Chemicals that may spill into water streams are often colorless and water-soluble, which makes it difficult to determine whether the water is polluted using the naked eye. When a chemical spill occurs, it is usually detected through a simple contact detection device by installing sensors at locations where leakage is likely to occur. The drawback with the approach using contact detection sensors is that it relies heavily on the skill of field workers. Moreover, these sensors are installed at a limited number of locations, so spill detection is not possible in areas where they are not installed. Recently hyperspectral images have been used to identify land cover and vegetation and to determine water quality by analyzing the inherent spectral characteristics of these materials. While hyperspectral sensors can potentially be used to detect chemical substances, there is currently a lack of research on the detection of chemicals in water streams using hyperspectral sensors. Therefore, this study utilized remote sensing techniques and the latest sensor technology to overcome the limitations of contact detection technology in detecting the leakage of hazardous chemical into aquatic systems. In this study, we aimed to determine whether 18 types of hazardous chemicals could be individually classified using hyperspectral image. To this end, we obtained hyperspectral images of each chemical to establish a spectral library. We expect that future studies will expand the spectral library database for hazardous chemicals and that verification of its application in water streams will be conducted so that it can be applied to real-time monitoring to facilitate rapid detection and response when a chemical spill has occurred.

• Economic and Environmental Geology
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• v.44 no.6
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• pp.463-474
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• 2011

The Haenam epithermal mineralized zone is located in the southwestern part of South Korea, and hosts low sulfidation epithermal Au-Ag deposit (Eunsan-Moisan) and clay quarries (Okmaesan, Seongsan, and Chunsan). Epithermal deposits and accompanying hydrothermal alteration related to Cretaceous volcanism caused large zoned assemblages of hydrothermal alteration minerals. Advanced argillic-altered rocks with mineral assemblages of alunite-quartz, alunite-dickite-quartz, and dickite-kaolinite-quartz exposed on the Okmaesan, Seongsan, and Chunsan area. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), with three visible and near infrared bands, six shortwave infrared bands, and five thermal infrared bands, was used to identify advanced argillic-altered rocks within the Haenam epithermal mineralized zone. The distinct spectral features of hydrothermal minerals allow discrimination of advanced argillic-altered rocks from non-altered rocks within the study area. Because alunite, dickite, and kaolinite, consisting of advanced argillic-altered rocks within the study area are characterized by Al-O-H-bearing minerals, these acid hydrothermal minerals have a strong absorption feature at $2.20{\mu}m$. The band combination and band ratio transformation cause increasing differences of DN values between advanced argillic-altered rock and non-altered rock. The alunite and dickite-kaolinite of advanced argillic-altered rocks from the Okmaesan, Seongsan, and Chunsan have average DN values of 1.523 and 1.737, respectively. These values are much higher than those (1.211 and 1.308, respectively) of non-altered area. ASTER images can remotely provide the distribution of hydrothermal minerals on the surface. In this way good relation between ASTER spectra analysis and field data suggests that ASTER spectral analysis can be useful tool in the initial steps of mineral exploration.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.351-361
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• 2024

Satellite imagery contains various elements such as clouds, cloud shadows, and terrain shadows. Accurately identifying and eliminating these factors that complicate satellite image analysis is essential for maintaining the reliability of remote sensing imagery. For this reason, satellites such as Landsat-8, Sentinel-2, and Compact Advanced Satellite 500-1 (CAS500-1) provide Usable Data Masks(UDMs)with images as part of their Analysis Ready Data (ARD) product. Precise detection of clouds and their shadows is crucial for the accurate construction of these UDMs. Existing cloud and their shadow detection methods are categorized into threshold-based methods and Artificial Intelligence (AI)-based methods. Recently, AI-based methods, particularly deep learning networks, have been preferred due to their advantage in handling large datasets. This study aims to analyze the applicability of constructing UDMs for high-resolution satellite images through deep learning-based cloud and their shadow detection using open-source datasets. To validate the performance of the deep learning network, we compared the detection results generated by the network with pre-existing UDMs from Landsat-8, Sentinel-2, and CAS500-1 satellite images. The results demonstrated that high accuracy in the detection outcomes produced by the deep learning network. Additionally, we applied the network to detect cloud and their shadow in KOMPSAT-3/3A images, which do not provide UDMs. The experiment confirmed that the deep learning network effectively detected cloud and their shadow in high-resolution satellite images. Through this, we could demonstrate the applicability that UDM data for high-resolution satellite imagery can be constructed using the deep learning network.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.5
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• pp.363-371
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• 2015

Feature extraction is one of the main goals in many remote sensing analyses. After high-resolution imagery became more available, it became possible to extract more detailed and specific features. Thus, considerable image segmentation algorithms have been developed, because traditional pixel-based analysis proved insufficient for high-resolution imagery due to its inability to handle the internal variability of complex scenes. However, the individual segmentation method, which simply uses color layers, is limited in its ability to extract various target features with different spectral and shape characteristics. Spectral indices can be used to support effective feature extraction by helping to identify abundant surface materials. This study aims to evaluate a feature extraction method based on a segmentation technique with spectral indices. We tested the extraction of diverse target features-such as buildings, vegetation, water, and shadows from eight band WorldView-2 satellite image using decision tree classification and used the result to draw the appropriate spectral indices for each specific feature extraction. From the results, We identified that spectral band ratios can be applied to distinguish feature classes simply and effectively.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.7
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• pp.597-606
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• 2020

Because the USV(Unmanned Surface Vehicle) is capable of remote control or autonomous navigation at sea, it can secure the superiority of combat power while minimizing human losses in a future combat environment. To plan the technology for the development of USV, the trend analysis of related technology and the selection of promising technology should be preceded, but there has been little research in this area. The purpose of this paper was to measure and evaluate the technology trends quantitatively. For this purpose, this study analyzed the technology trends and selected promising/declining technologies using topic modeling of papers and patent data. As a result of topic modeling, promising technologies include control and navigation, verification/validation, autonomous level, mission module, and application technology, and declining technologies include underwater communication and image processing technology. This study also identified new technology areas that were not included in the existing technology classification, e.g., technology related to research and development of USV, artificial intelligence, launch/recovery, and operation, such as cooperation with manned and unmanned systems. The technology trends and new technology areas identified through this study may be used to derive key technologies related to the development of the USV and establish appropriate R&D policies.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.269-272
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• 2015

this is the study of the human body antenna device which can detect the powder in a 360-degree on(under) the ground whether it is metal or nonmetal using superhigh frequency RF beam equipped with the body. and it is able to transmit the data of the detection of the powder, battle combats can share that among them. with its flexible roof radial antenna structure, it emits the superhigh frequency RF beam to the front and flank multiply, preprocesses through the powder preprocessing part. and with the non-linear regression model algorism engine part, reflecting the attenuation characteristics depend on the delayed time of degree of the signal power which is received to the superhigh frequency RF beam. so it is able to detect the signal of the most likely mine or powder based on the degree of the answer signal power according to the delayed time of the superhigh frequency RF beam. also, it can detect the powder whether it is metal or nonmetal, mine, dud, VBIED. it can increase the chance of detection about 90% more than existing mine detector.

• Journal of the Korean Society of Marine Environment & Safety
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• v.14 no.2
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• pp.163-168
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• 2008

Ship detection from satellite remote sensing is a crucial application for global monitoring for the purpose of protecting the marine environment and ensuring marine security. It permits to monitor sea traffic including fisheries, and to associate ships with oil discharge. An automatic ship detection approach for RADARSAT Fine Synthetic Aperture Radar (SAR) image is described and assessed using in situ ship validation information collected during field experiments conducted on August 6, 2004. Ship detection algorithms developed here consist of five stages: calibration, land masking, prescreening, point positioning, and discrimination. The fine image was acquired of Ulsan Port, located in southeast Korea, and during the acquisition, wind speeds between 0 m/s and 0.4 m/s were reported. The detection approach is applied to anchoring ships in the anchorage area of the port and its results are compared with validation data based on Vessel Traffic Service (VTS) radar. Our analysis for anchoring ships, above 68 m in length (LOA), indicates a 100% ship detection rate for the RADARSAT single beam mode. It is shown that the ship detection performance of SAR for smaller ships like barge could be higher than the land-based radar. The proposed method is also applied to estimate the ship's dimensions of length and breadth from SAR radar cross section(RCS), but those values were comparatively higher than the actual sizes because of layover and shadow effects of SAR.

• Journal of Korean Society of Forest Science
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• v.76 no.1
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• pp.41-50
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• 1987

The various conditions of photographs, especially kinds of films, combinated filters and seasons are important factors for forestry purpose aerial photography. In this paper the variations of tones were compared between color and color infrared, panchromatic black and white and infrared black and white, and among false color photographic images created by using 3 kinds of filters when prints are made. Color infrared film was good for identifying tree species, for its spectral signatures had a greater range of tones and hues than color signatures. In that case taken in May were more effective than taken April. False color photographs were not so good as color photographs because they were mostly dark and indistinct. Infrared black and white film with medium red filter showed potential for separating broad-leaved forests from conifers. MSS composed photographs, when composed with proper bands and densities, were proved useful for distinguishing land use types but not applicable to more detailed practices such as forest type separation and tree species identification.