• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.2
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• pp.147-155
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• 2024

Based on the recently developed deep learning technology, many studies have been conducted on deep learning networks that simultaneously detect and classify targets of interest in synthetic aperture radar(SAR) images. Although numerous research results have been derived mainly with the open SAR ship datasets, there is a lack of work carried out on the deep learning network aimed at detecting and classifying SAR ground targets and trained with the synthetic dataset generated from electromagnetic scattering simulations. In this respect, this paper presents the deep learning network trained with the synthetic dataset and applies it to detecting and classifying real SAR ground targets. With experiment results, this paper also analyzes the network performance according to the composition ratio between the real measured data and the synthetic data involved in network training. Finally, the summary and limitations are discussed to give information on the future research direction.

• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.2
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• pp.97-105
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• 2020

In order to set up radar cross section(RCS) reduction factors for a target, the scattering point position of the target should be identified through inverse synthetic aperture radar(ISAR) image analysis. For this purpose, ISAR image focusing is important. Maritime ship is non-linear maneuvering in the sea, however, which blur the ISAR image. To solve this problem, translational and rotational motion compensation are essential to form focused ISAR image. In this paper, hourglass and ISAR image analysis are performed on the collected data in the sea instead of using the prediction software tool, which takes much time and cost to make computer-aided design(CAD) model of the ship.

• Aerospace Engineering and Technology
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• v.8 no.1
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• pp.132-137
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• 2009

This study has regard to classification by using multi-temporal SAR data. Multi-temporal JERS-1 SAR images are used for extract the land cover information and possibility. So far, land cover information extracted by high resolution aerial photo, satellite images, and field survey. This study developed on multi-temporal land cover status monitoring and coherence information mapping can be processing by L band SAR image. From July, 1997 to October, 1998 JERS SAR images (9 scenes) coherence values are analyzed and then extracted land cover information factors, so on. This technique which forms the basis of what is called SAR Interferometry or InSAR for short has also been employed in spaceborne systems. In such systems the separation of the antennas, called the baseline is obtained by utilizing a single antenna in a repeat pass.

• New & Renewable Energy
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• v.9 no.3
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• pp.14-19
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• 2013

Comparative evaluation of offshore wind retreival software, which use the satellite images taken by Synthetic Aperture Radar sensor; SARTools of CLS-SOPRONO, France and SpaceEye of London Research and Development Corporation, Canada is carried out. For a reference satellite image, ENVISAT ASAR imagery of Jeollanam-do Wan-do area when the winter-time northwestern wind prevails is processed by CMOD_IFR2, CMOD4, CMOD5 algorithms. Wind speed difference and its relative ratio are calculated to evaluate uncertainty of software selection.

• 한국신재생에너지학회:학술대회논문집
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• 2008.10a
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• pp.318-320
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• 2008

Offshore remote-sensing wind extraction using SAR satellite image is an emerging and promising technology for offshore wind resource assessment. We compared our numerical weather prediction and offshore wind extraction from ENVISAT images around Korea offshore areas. A few comparison sets showed good agreement but more comparisons are required to draw application guideline on a statistical basis.

• Aerospace Engineering and Technology
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• v.10 no.2
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• pp.163-169
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• 2011

This paper introduces a method about water line detection using SAR image. The method includes pre-processing of the SAR image with the threshold of the histogram to recognize the boundary between the water and the landmass area. Finally we applied the image differentiation to detect the water line in the SAR image. The TerraSAR-X and the Cosmo-SkyMed images, which are the high spatial resolution X-band SAR data, are used for the evaluation of our algorithm. The accuracy is verified over the stream line in urban area with the result from the Cosmo-SkyMed.

• Proceedings of the KSRS Conference
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• 2009.03a
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• pp.143-146
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• 2009

다산기지 주변 해역인 스발바드섬은 북반구 해빙 분포지역의 가장자리에 위치해 있으며 해빙의 이동이 비교적 빠른 지역이다. 지구 온난화의 영향을 받는 대표적인 지역으로 이 지역의 해빙변화에 대한 연구는 지구온난화의 지표로서 중요성을 가진다. 스발바드섬 주변의 해빙에서 얻어진 다편파 SAR 데이터를 분석하여 해빙에 대한 후방산란계수의 특성을 분석하고자 한다. 데이터 획득에는 ENVISAT/ASAR (2002 년 발사 C-밴드, 다편파 사용)과 PALSAR (2006 년 발사, L-밴드, 다편파 사용)의 두 가지 SAR 가 이용되었으며 데이터 획득 시기는 해빙의 변화가 활발한 4 월경이다. 기본적으로 L-밴드와 C-밴드의 두 가지 밴드별 차이에 관한 특성을 알아보고 기타 후방산란계수에 영향을 주는 요소들에 대하여 알아보고자 한다.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.3
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• pp.461-469
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• 2010

In this paper, we have proposed an improved fusion method of detection features which can enhance the detection probability under the given false alarm rate in the prescreening stage of SAR ATR(Synthetic Aperture Radar Automatic Target Recognition) system. Since the detection features have the positive correlation, the detection performance can be improved if the joint probability distribution of detection features is considered in the fusion process. The detection region is designed as a simple piecewise linear function which can be represented by few parameters. The parameters for the detection region can be derived by training the sample SAR images to maximize the detection probability with the given false alarm rate. Simulation result shows that the detection performance of the proposed method is improved for all combinations of detection features.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2009.06a
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• pp.21-22
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• 2009

북극의 환경은 전 지구적으로 환경변화에 민감하다. 그중 해빙의 증가 및 감소는 지구의 온난화 진행의 지표로 작용을 한다. 본 연구에서는, 수동 마이크로파 센서인 AMSR-E를 이용하여 북극을 중심으로 북위 $60^{\circ}$ 이내의 지역에서 2002년$\sim$2009년 동안의 북극해빙 면적에 관한 데이터를 획득하고 북극 해빙 전체면적의 증가 및 감소에 관한 변화를 관측하였다. 추가로, 고해상도인 합성개구레이더를 이용하여 북극 다산기지 주변 해역에 대해 얻어진 데이터의 결과에 대해 고찰한다. 구체적인 연구 내용은 다음과 같다. 1. 북극 해빙 면적의 연변동 조사, 2. 북극 해역별 해빙 분포 특성, 3. 해빙 변동 특성 해석, 4. 향후, 활용 방향 소개: 온난화에 따른 북극 기후 변화, 북극 자원탐사 및 항로 개발 적용 방안, 5. SAR의 주파수별, 편파별 해빙 산란 특성 기초 조사 및 향후 활용 방향, 6. 북극 현장 관측 프로그램 소개.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2009.06a
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• pp.23-24
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• 2009

허베이스피리트호 원유유출 사고는 2007년 12월7일 아침 7시6분경 서해안 만리포 북서쪽 10km 해상에서 크레인을 적재한 1만1800t급 바지선이 정박 중인 홍콩 선적 유조선 허베이 스피리트호(14만6000t급)와 부딪치면서 발생했다. 이와 같은 기름 유출 사고의 경우, 유출 범위를 정확하게 이해하는 것이 중요하다. 여기서는 위 사고 기간에 얻어진 인공위성 자료를 이용하여 기름 유출을 탐지하기 위한 연구결과를 소개한다. 광학과 마이크로파데이터에 대해 유출 범위의 계산 및 해석 알고리듬에 대한 현재까지의 결과를 소개한다. 광학데이터로는 아리랑 2호(다목적실용위성 2호, KOMPSAT II)) MSC(Multi Spectral Camera)자료가 사용되었으며, 합성개구레이더로는 ENVISAT ASAR, TerraSAR-X 및 ALOS PALSAR의 자료가 사용되었다.