• Proceedings of the Korean Society of Computer Information Conference
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• 2010.07a
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• pp.319-322
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• 2010

본 논문에서는 저가의 비용으로 구축이 가능한 USB 인터페이스용 PC 카메라를 이용한다. 사람의 침입이나 사람의 움직임을 감시할 필요가 있는 장소에 카메라를 설치하여 영상을 계속 감시한다. 감시가 필요한 장소에 설치된 각 카메라의 영상에 변화를 저장하여 기록하는데 있어서, 비교적 적은 비용이 필요하다. 또한 감시가 필요한 장소를 보다 안전하고 정확하게 감시할 수 있는 무인 침입탐지시스템에 영상처리와 영상인식 기술을 이용하여 실시간 감시시스템을 구현한다. 구현한 시스템은 웹을 기반으로 다양한 원격지의 화상 자료의 신속한 전송, 정확성의 구현, 특정 움직임의 캡처 및 선택, 검색, 자동 움직임 감지 등의 장점을 제공한다. 또한 독자적 시스템을 제공하여 다수의 시스템을 영상 입력 서버로 이용이 가능하도록 하였다. 뿐만 아니라, 서버에 C/S 형태의 시스템도 함께 제공하여, 영상인식 모듈을 탑재할 수도 있다. 덧붙여 인터넷을 통한 자료의 전송기술 및 QoS 만족을 위한 자료의 압축 및 품질 향상 기술을 적용하여 원격 출력과 원격 전송이 가능하여 저장 장치의 유지 관리 및 설치면에서 많은 경제적 이점이 있다.

• Korean Journal of Remote Sensing
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• v.34 no.2_2
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• pp.327-338
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• 2018

The possibility of ship detection monitoring at operational level using KOMPSAT-5 Synthetic Aperture Radar (SAR) and Automatic Identification System (AIS) data is investigated. For the analysis, the KOMPSAT-5 SLC images, which are collected from the west coast of Shinjin port and the northern coast of Jeju port are used along with portable AIS data from near the coast. The ship detection algorithm based on HVAS (Human Visual Attention System) was applied, which has significant advantages in terms of detection speed and accuracy compared to the commonly used CFAR (Constant False Alarm Rate). As a result of the integrated analysis, the ship detection from KOMPSAT-5 and AIS were generally consistent except for small vessels. Some ships detected in KOMPSAT-5 but not in AIS are due to the data absence from AIS, while it is clearly visible in KOMPSAT-5. Meanwhile, SAR imagery also has some false alarms due to ship wakes, ghost effect, and DEM error (or satellite orbit error) during object masking in land. Improving the developed ship detection algorithm and collecting reliable AIS data will contribute for building wide integrated surveillance system of marine territory at operational level.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1083-1094
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• 2021

In order to monitor nuclear activity in inaccessible areas, it is necessary to establish a methodology to analyze changesin nuclear activity-related objects using high-resolution satellite images. However, traditional object detection and change detection techniques using satellite images have difficulties in applying detection results to various fields because effects of seasons and weather at the time of image acquisition. Therefore, in this paper, an object of interest was detected in a satellite image using a deep learning model, and object changes in the satellite image were analyzed based on object detection results. An initial training of the deep learning model was performed using an open dataset for object detection, and additional training dataset for the region of interest were generated and applied to transfer learning. After detecting objects by multitemporal and multisensory satellite images, we tried to detect changes in objects in the images by using them. In the experiments, it was confirmed that the object detection results of various satellite images can be directly used for change detection for nuclear activity-related monitoring in inaccessible areas.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.177-198
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• 2021

Existing change detection researches have been focused on changes of land use and land cover (LULC), damaged areas, or large vegetated and water regions. On the other hands, increased temporal and spatial resolution of satellite images are strongly suggesting the feasibility of change detection of small objects such as vehicles and ships. In order to check the feasibility, this paper analyzes the performance of existing pixel-based change detection methods over small objects. We applied pixel differencing, PCA (principal component analysis) analysis, MAD (Multivariate Alteration Detection), and IR-MAD (Iteratively Reweighted-MAD) to Kompsat-3A and Google Map images taken within 10 days. We extracted ground references for changed and non-changed small objects from the images and used them for performance analysis of change detection results. Our analysis showed that MAD and IR-MAD, that are known to perform best over LULC and large areal changes, offered best performance over small object changes among the methods tested. It also showed that the spectral band with high reflectivity of the object of interest needs to be included for change analysis.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1579-1590
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• 2020

In this paper, spatially-adaptive denormalization (SPADE) based U-Net is proposed to detect changes by using high-resolution satellite images. The proposed network is to preserve spatial information using SPADE. Change detection methods using high-resolution satellite images can be used to resolve various urban problems such as city planning and forecasting. For using pixel-based change detection, which is a conventional method such as Iteratively Reweighted-Multivariate Alteration Detection (IR-MAD), unchanged areas will be detected as changing areas because changes in pixels are sensitive to the state of the environment such as seasonal changes between images. Therefore, in this paper, to precisely detect the changes of the objects that consist of the city in time-series satellite images, the semantic spatial objects that consist of the city are defined, extracted through deep learning based image segmentation, and then analyzed the changes between areas to carry out change detection. The semantic objects for analyzing changes were defined as six classes: building, road, farmland, vinyl house, forest area, and waterside area. Each network model learned with KOMPSAT-3A satellite images performs a change detection for the time-series KOMPSAT-3 satellite images. For objective assessments for change detection, we use F1-score, kappa. We found that the proposed method gives a better performance compared to U-Net and UNet++ by achieving an average F1-score of 0.77, kappa of 77.29.

• Proceedings of the Optical Society of Korea Conference
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• 2005.02a
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• pp.348-349
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• 2005

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2002.10a
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• pp.225-228
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• 2002

• Proceedings of the Optical Society of Korea Conference
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• 2006.02a
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• pp.213-214
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• 2006

• Proceedings of the Korea Contents Association Conference
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• 2003.05a
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• pp.194-197
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• 2003

인터넷과 네트워크기술이 발전함에 따라 전자적 거래 등의 사이버마켓에 관한 응용분야가 운영되고 있으며, 실제 오프라인 마켓 보다 더 많은 수익을 내는 쇼핑몰 운영업체들이 나타나고 있다. 또한 원격지의 구매자로부터 인터넷 뱅킹에 의한 쉽고 빠른 결재 방식으로 인하여 구매, 결재에서 배송까지 하나의 프로세스라인을 구축하고 있다. 이에 따른 사용자 정보를 보호하기 위한 여러 가지 암호화 기법들이 연구 및 도입되고 있다. 따라서 인증 되지 않은 외부 침입자로부터 시스템의 정보보호를 위하여 침입탐지 시스템 및 암호화, 복호화 알고리즘을 적용하여 소프트웨어 측면에서의 보안기법과 Firewall 등의 하드웨어적인 보안기술이 도입 및 실용화 되고 있는 것이다. 본 논문 에서는 우리 주변에서 발생 하고 있는 해킹 사고 사례와 나날이 발전해가는 해킹 방법의 동향, 이러한 불법 침입을 이용한 침입 탐지 기법들과 네트워크에 관련한 침입탐지 방안기술 그리고 라우터 장비에서의 침입탐지 방안을 제시 하였다.

• Journal of the Korea Institute of Military Science and Technology
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• v.10 no.3
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• pp.129-138
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• 2007

Recently, Direction Finding(DF) System is using switching DF algorithm to reduce system-weight by eliminating RF cable as much as possible. Also, Correlation Vector Direction Finding(CVDF) algorithms is being used for Fast Direction finding in tactical environment. In this paper, I will give you burst error elimination algorithms and compare the performance in case we use switching CVDF algorithm. Although antenna array is not working, we will successfully perform direction finding when we use this burst error elimination algorithms. Also, we will be completely capable of DF mission despite of meeting the unwanted situation that the monitoring signal disappear in case we use Switching Direction Finding algorithms. That situation frequently occurs under the Frequency Hopping signal circumstances.