• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2007년도 춘계학술대회 논문집
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• pp.76-81
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• 2007

융설 모형의 중요 매개변수인 적설분포면적은 실제 우리나라에서 적설과 관련한 관측 자료의 부족으로 인해 매개변수 추정이 어렵다. 이러한 문제점 해결을 위해 원격탐사기법을 활용하여 적설분포면적을 추출하였다. 본 연구에서는 1997년 부터 2006년 까지의 겨울철 NOAA (National Oceanic and Atmospheric Administration)의 AVHRR(Advanced Very High Resolution Radiometer) 위성영상의 8 sets의 총 108개 영상을 이용하여 적설분포면적을 추출하였고，기상청의 지상기상관측소의 최섬적설심 자료를 이용하여 GIS 자료를 구축함으로써 적설심의 공간적 분포를 추출하였다. 이를 국내 5대유역인 한강，낙동강，금강，영산강，섬진강 유역에 대하여 융설모형의 주요 매개변수인 적설분포면적，유역 평균， 최대 적설심과 적설분포감소비곡선을 구축하였다. 그 중 적설분포면적감소곡선 (SDC : Snow cover Depletion Curve)는 적설분포면적의 감소형태를 나타내 주는 지표로써 융설의 가장 민감한 매개변수이다. 이를 국내 5대 강 유역에 대해 구축하여 정량화 하였다.

• Structural Engineering and Mechanics
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• 제46권6호
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• pp.869-886
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• 2013

This work presents an experimental methodology specially developed for the nonlinear large-amplitude free vibration analysis of a clamped-free thin-walled metal column under self-weight. The main contribution of this paper is related to the developed experimental methodology which is based on a remote sensing technique using a computer vision system that integrates, on-line, the digital image acquisition and its treatment through special image processing routines. The main importance of this methodology is that it performs large deflections measurements without making contact with the structure and thus, not introducing undesirable changes in its behavior, for instance, appreciable changes in mass and stiffness properties. This structure presents, in most cases, highly non-linear responses, which cannot be reproduced by conventional finite-element softwares due, mainly, to the simultaneous influence of geometric and inertial non-linearities. To capture the non-linearities associated with large amplitude vibration and be able to describe the buckling process, the structure is discretized as a sequence of jointed coupled elastic pendulums. The obtained numerical results are favorably compared with the experimental ones, in the pre- and post-buckling regimes.

• 전자공학회논문지
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• 제53권9호
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• pp.118-125
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• 2016

방사능 누출 사고 시 대응이나 핵안보 검증을 위한 핵물질 탐지에 있어서, 방사선을 방출하는 미지의 선원에 대한 위치 정보를 파악하는 것은 중요하다. 그러한 기구 중 하나인 회전 변조 시준기는 미지 선원을 원격 감지하기 위한 장비로서 영상화를 통해 선원의 위치 탐지가 가능하다. 본 논문에서는 Kowash의 연구를 기초로 회전 변조 시준기의 시스템 모델과 그를 영상화하는 알고리즘을 소개한다. 하지만 결과 영상화 이미지는 선원의 방향은 보여줄 수 있으나 선원의 거리를 찾지 못하는 문제점이 있다. 또한 선원의 실제 방향뿐 아니라 $180^{\circ}$ 대칭방향에서도 선원을 추정하는 모호성 문제를 안고 있다. 본 논문에서 우리는 영상화 결과의 방향 대칭적 모호성을 해결하고, 두 대의 RMC를 이용해 거리를 추정하는 방법을 제안한다. 그리고 이를 RMC 시뮬레이션 데이터를 이용하여 성능을 검증한다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2008년도 International Symposium on Remote Sensing
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• pp.188-191
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• 2008

The Tonle Sap, Cambodia, is a huge lake and periodically flooded due to monsoon climate. The incoming water causes intensive flooding that expands the lake over vast floodplain and wetland consisting mainly of forests and shrubs. Monitoring the water-level change over the floodplain is essential for flood prediction and water resource management. A main objective of this study is flood monitoring over Tonle Sap area using ALOS PALSAR. To study double-bounce effects in the lake, backscattering effect using ALOS PALSAR dual-polarization (HH, HV) data was examined. InSAR technique was applied for detection of water-level change. HH-polarization interferometric pairs between wet and dry seasons were best to measure water level change around northwestern parts of Tonle Sap. The seasonal pattern of water-level variations in Tonle Sap studied by InSAR method is similar to the past and altimeter data. However, water level variation measured by SAR was much smaller than that by altimeter because the DInSAR measurement only represents water level change at a given region of floodplain while altimeter provides water level variation at the central parts of the lake.

• Journal of Advanced Marine Engineering and Technology
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• 제38권10호
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• pp.1275-1280
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• 2014

In this study, the performance of a real-time micro telescopic monitoring system is evaluated, in which an artificial neural network is adopted for the diagnoses of vibratory bodies, such as solid piping system or machinery. The structural vibration was measured by a non-contact remote sensing method, in which images of a high-speed high-definition camera were used. The structural vibration data that can be obtained by the PIV (particle image velocimetry) technique were used for training the neural network. The structures of the neural network are dynamically changed and their performances are evaluated for the constructed diagnosis system. Optimized structures of the neural network are proposed for real-time diagnosis for the piping system. It was experimentally verified that the performances of the neural network used for real-time monitoring are influenced by the types of the vibration data, such as minimum, maximum and average values of the vibration data. It concludes that the time-mean values are most appropriate for monitoring the piping system.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2007년도 Proceedings of ISRS 2007
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• pp.254-257
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• 2007

This paper presents an examination of the spatial integration method for extracting the RCS of a trihedral corner reflector from SAR images for SAR external calibration. An exact external radiometric calibration technique is required for extracting an exact calibration constant. Therefore, we examine the accuracy of the spatial integration method, which is commonly used for the SAR external radiometric calibration. At first, an SAR image for a trihedral corner reflector is simulated with a high-resolution SAR impulse response with a known theoretical RCS of the reflector, and a background clutter image for the high resolution SAR system is also generated. Then, a SAR image in a high resolution is generated for a trihedral comer reflector located on a background clutter by superposition of the two SAR images. The radar cross section of a trihedral corner reflector in the SAR image is retrieved by integrating the radar signals of the pixels adjacent to the reflector for various size of the integration area. By comparison of the measured RCS by the integration method and the theoretical RCS of the reflector, the effect of the size of the integration area on the extraction of the calibration constant is examined.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2008년도 춘계학술대회 논문집
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• pp.158-163
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• 2008

In inverse synthetic aperture radar (ISAR) imaging, An ISAR autofocusing algorithm is essential to obtain well-focused ISAR images. Traditional methods have relied on the approximation that the phase error due to target motion is a function of the cross-range dimension only. However, in the stepped-frequency radar system, it tends to become a two-dimensional function of both down-range and cross-range, especially when target's movement is very fast and the pulse repetition frequency (PRF) is low. In order to remove the phase error along down-range, this paper proposes a method called SAEM (subarray averaging and entropy minimization) [1] that uses a subarray averaging concept in conjunction with the entropy cost function in order to find target motion parameters, and a novel 2-D optimization technique with the inherent properties of the proposed entropy-based cost function. A well-focused ISAR image can be obtained from the combination of the proposed method and a traditional autofocus algorithm that removes the phase error along the cross-range dimension. The effectiveness of this method is illustrated and analyzed with simulated targets comprised of point scatters.

• 한국측량학회지
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• 제21권3호
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• pp.261-267
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• 2003

두 시기의 분류영상으로부터 추출된 각각의 정보를 지리정보의 속성정보로 입력하여 지리정보시스템의 한 매개변수로 이용함으로써 두 시기의 지표변화정보를 동시에 추출할 수 있다. 따라서 본 연구에서는 지표피복 정보를 획득할 수 있는 원격탐사기법과 지리정보시스템의 장점을 활용하여 토지이용 변화양상과 더불어 지표의 효율적인 관리 및 개발을 위한 기초자료를 제시하고자 하였다 지표변화정보 추출에 관한 연구를 수행한 결과 기존의 영상분류방법보다 영상정보처리를 쉽게 할 수 있어 각 화소에 대한 지표의 변화과정을 쉽게 알 수 있었다.

• 한국측량학회지
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• 제34권2호
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• pp.195-206
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• 2016

The remote sensing technique using SAR data have many advantages when applied to the disaster site due to its wide coverage and all-weather acquisition availability. Although a single-pol (polarimetric) SAR image cannot represent the land surface better than a quad-pol SAR image can, single-pol SAR data are worth using for disaster-induced change detection. In this paper, an automatic change detection method based on a mixture of GGDs (generalized Gaussian distribution) is proposed, and usability of the textural features and intensity is evaluated by using the proposed method. Three ALOS/PALSAR images were used in the experiments, and the study site was Norita City, which was affected by the 2011 Tohoku earthquake. The experiment results showed that the proposed automatic change detection method is practical for disaster sites where the large areas change. The intensity information is useful for detecting disaster-induced changes with a 68.3% g-mean, but the texture information is not. The autocorrelation and correlation show the interesting implication that they tend not to extract agricultural areas in the change detection map. Therefore, the final tsunami-induced change map is produced by the combination of three maps: one is derived from the intensity information and used as an initial map, and the others are derived from the textural information and used as auxiliary data.

• Korean Journal of Geomatics
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• 제5권1호
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• pp.27-34
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• 2005

Unsupervised classification is an important area of research in image processing because supervised classification has the disadvantages such as long task-training time and high cost and low objectivity in training information. This paper focuses on unsupervised classification, which can extract ground object information with the minimum 'Spectral Angle Distance' operation on be behalf of 'Spectral Euclidian Distance' in the clustering process. Unlike previous studies, our algorithm uses the unit vector, not the spectral distance, to compute the cluster mean, and the Single-Pass algorithm automatically determines the seed points. Atmospheric correction for more accurate results was adapted on the Hyperion data and the results were analyzed. We applied the algorithm to the Hyperion and ETM+ data and compared the results with K-Means and the former USAM algorithm. From the result, USAM classified the water and dark forest area well and gave more accurate results than K-Means, so we believe that the 'Spectral Angle' can be one of the most accurate classifiers of not only multispectral images but hyperspectral images. And also the unit vector can be an efficient technique for characterizing the Remote Sensing data.