• Proceedings of the KIEE Conference
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• 2002.07b
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• pp.870-872
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• 2002

본 연구는 탐지 대상물체의 형상인식이 가능한 비접촉, 원격 탐지장치의 개발에 관한 것이다. 형체인식용 비접촉 시스템은 주로 Magneto- lmpedance법과 Magneto-Inductance법이 있으며, Magneto-Inductance법은 왜곡이 적고, 인체나 탐지 대상물체에 손상을 가져오지 않는다. 본 연구에서는 2극으로 디자인 된 Magnetic Tomography System을 이용하여 물체의 위치에 따른 투자율 변화량을 계산 및 실측하였다.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2002.05a
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• pp.87-91
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• 2002

본 연구는 탐지 대상물체의 형상인식 이 가능한 비접촉, 원격 탐지장치의 개발에 관한 것이다. 형체인식용 비접촉 시스템은 주로 Magneto-Impedance법과 Magneto-Inductance법이 있 으며, Magneto- Inductance법은 왜곡이 적고, 인체나 탐지대상물체에 손상을 가져오지 않는다. 본 연구에서는 2극으로 디자인 된 Magnetic Tomography System을 이용하여 물체의 위치에 따른 투자율 변화량을 계산 및 실측하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.224-224
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• 2021

광학원격탐사를 통해 취득할 수 있는 초분광 영상은 관련 기술의 발전으로 다양하게 활용이 되고 있다. 특히 초경량 UAV를 기반으로 초분광 센서를 적용한 광학원격탐사는 광범위하게 분포하는 국내 제방의 불안정 요소를 탐지하는데 보다 효과적일 것으로 판단되며 대상에 대한 광역모니터링을 통해 많은 자료를 얻을 수 있고, 고해상도 영상 자료를 활용한 세밀한 분광 및 공간정보 분석이 가능하다. 본 연구에서는 제방 균열 평가를 위해 UAV를 활용하여 안동댐 하류 제방 균열을 대상으로 고해상도 초분광 영상을 취득하였으며, 기 개발된 제방 균열 평가 소프트웨어를 이용하여 조도와 최대강도 데이터에 따른 제방 균열 평가를 실시하였다. 연구지역의 지질은 중생대 백악기의 일직층으로써 적색이암, 셰일, 역질사암 등이 주를 이루고 있으며 제방 내 토양은 대부분 입도가 균일하며 일부 역암이 관찰되는 지역으로 조립토가 주를 이루고 있다. 기 개발된 소프트웨어의 특징은 측정된 데이터를 바탕으로 균열 여부를 판별할 수 있는 프로그램으로써 측정지점마다 별도의 조도와 최대강도 데이터가 주어졌을때, 해당 데이터에 대한 균열 여부를 판별할 수 있다. 주요기능은 제방 균열 여부 판단, 데이터 입력 및 판단을 출력하기 위한 GUI 인터페이스를 제공한다. 연구 결과 제방 균열 평가 소프트웨어를 적용하여 균열과 비균열에 대한 탐지가 가능한 것으로 나타났다. 특히 비균열 포인트의 경우 암석이나 토양의 성질, 빛의 반사에 따라 일부 차이가 있지만 균열은 매우 유사한 반사율 정보를 보이는 것으로 나타났다.

• Korean Journal of Remote Sensing
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• v.36 no.2_2
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• pp.365-377
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• 2020

Continuous monitoring and immediate response is essential to protect the national maritime territory and maritime resources from the activities of illegal ships. Synthetic Aperture Radar (SAR) images with a wide range of images are effective for maritime surveillance asthe weather and day-night conditions rarely affect to image acquisition. However, an effective ship detection is not easy due to the huge data size of SAR images and various characteristics such as the speckle noise. In this study, the Human Visual Attention System (HVAS) algorithm was applied to KOMPSAT-5 to extract the initial targets, and the SAR-Split algorithm depending on the imaging modes was used to remove false alarms. The detected targets were finally selected by the Constant False Alarm Rate (CFAR) algorithm and matched with the ship's Automatic Identification System (AIS) information. Overall, the detected targets were well matched with AIS data, but some false alarms by ship wakes were observed. The detection rate was about 80% in ES mode and about 64% in ST mode. It is expected that the developed ship detection algorithm will contribute to the construction of a wide area maritime surveillance network.

• The KIPS Transactions:PartC
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• v.19C no.1
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• pp.47-54
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• 2012

Sharing cross-site resources has been adopted by many recent websites in the forms of service-mashup and social network services. In this change, exploitation of the new vulnerabilities increases, which includes inserting malicious codes into the interaction points between clients and services instead of attacking the websites directly. In this paper, we present a system model to identify malicious script codes in the web contents by means of a remote verification while the web contents downloaded from multiple trusted origins are executed in a client's browser space. Our system classifies verification items according to the origin of request based on the information on the service code implementation and stores the verification results into three databases composed of white, gray, and black lists. Through the experimental evaluations, we have confirmed that our system provides clients with increased security by effectively detecting malicious scripts in the mashup web environment.

• Korean Journal of Remote Sensing
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• v.25 no.2
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• pp.193-203
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• 2009

Snow cover is one of the important parameters since it determines surface energy balance and its variation. To classify snow and cloud from satellite data is very important process when inferring land surface information. Generally, misclassified cloud and snow pixel can lead directly to error factor for retrieval of surface products from satellite data. Therefore, in this study, we perform algorithm for detecting snow cover area with remote sensing data. We just utilize visible reflectance, and infrared channels rather than using NDSI (Normalized Difference Snow Index) which is one of optimized methods to detect snow cover. Because COMS MI (Meteorological Imager) channels doesn't include near infra-red, which is used to produce NDSI. Detecting snow cover with visible channel is well performed over clear sky area, but it is difficult to discriminate snow cover from mixed cloudy pixels. To improve those detecting abilities, brightness temperature difference (BTD) between 11 and 3.7 is used for snow detection. BTD method shows improved results than using only visible channel.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1363-1372
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• 2021

Red tide, defined as the major reproduction of harmful birds, has the characteristics of being generated and diffused in a wide area. This has limitations in detection only with the existing investigation method. Therefore, in this study, red tide was detected using a remote sensing technique. In addition, it was intended to increase the accuracy of detection by using optical characteristics, not just the concentration of chlorophyll. Red tide mainly occurs on the southern coast where sea signals are complex, and the main red tide control species on the southern coast is Cochlodinium polykirkoides. Therefore, it was intended to secure objectivity by reflecting features that could not be found depending on the researcher's observation and experience, not limited to visual judgment using machine learning techniques. In this study, support background machines and random forest were used among machine learning models, and as a result of calculating accuracy as performance evaluation indicators of the two models, the accuracy was 85.7% and 80.2%, respectively.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1353-1369
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• 2023

In the event of a disaster accident at sea, the scale of damage will vary due to weather effects such as wind, currents, and tidal waves, and it is obligatory to minimize the scale of damage by establishing appropriate control plans through quick on-site identification. In particular, it is difficult to identify pollutants that exist in a thin film at sea surface due to their relatively low viscosity and surface tension among pollutants discharged into the sea. Therefore, this study aims to develop an algorithm to detect suspended pollutants on the sea surface in RGB images using imaging equipment that can be easily used in the field, and to evaluate the performance of the algorithm using input data obtained from actual waters. The developed algorithm uses image enhancement techniques to improve the contrast between the intensity values of pollutants and general sea surfaces, and through histogram analysis, the background threshold is found,suspended solids other than pollutants are removed, and finally pollutants are classified. In this study, a real sea test using substitute materials was performed to evaluate the performance of the developed algorithm, and most of the suspended marine pollutants were detected, but the false detection area occurred in places with strong waves. However, the detection results are about three times better than the detection method using a single threshold in the existing algorithm. Through the results of this R&D, it is expected to be useful for on-site control response activities by detecting suspended marine pollutants that were difficult to identify with the naked eye at existing sites.

• Bulletin of the Korean Space Science Society
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• 1997.04a
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• pp.34.1-34
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• 1997

• Korean Journal of Remote Sensing
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• v.40 no.2
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• pp.151-166
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• 2024

Coastal debris presents a significant environmental threat globally. This research sought to improve the monitoring methods for coastal debris by employing deep learning and remote sensing technologies. To achieve this, an object detection approach utilizing the You Only Look Once (YOLO)v8 model was implemented to develop a comprehensive image dataset for 11 primary types of coastal debris in our country, proposing a protocol for the real-time detection and analysis of debris. Drone imagery was collected over Sinja Island, situated at the estuary of the Nakdong River, and analyzed using our custom YOLOv8-based analysis program to identify type-specific hotspots of coastal debris. The deployment of these mapping and analysis methodologies is anticipated to be effectively utilized in managing coastal debris.