• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1064-1066
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• 2003

In this paper, ground penetrating radar (GPR), which has the capability to detect non metal and plastic mines, is proposed to detect and discriminate antipersonnel (AP) landmines. The time domain GPR - Impulse radar and frequency domain GPR - SFCW (Stepped Frequency Continuous Wave) radar is utilized for metal and non-metal landmine detection and its performance is investigated. Since signal processing is vital for target reorganization and clutter rejection, we implemented the MUSIC (Multiple Signal Classification) algorithm for the signal processing of SFCW radar data and SAR (Synthetic Aperture Radar) processing method for the signal processing of Impulse radar data.

• Korean Journal of Remote Sensing
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• v.31 no.3
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• pp.227-244
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• 2015

Synthetic Aperture Radar is able to provide images of wide coverage in day, night, and all-weather conditions. Recently, as the SAR image resolution improves up to the sub-meter level, their applications are rapidly expanding accordingly. Especially there is a growing interest in the use of geographic information of high resolution SAR images and the change detection will be one of the most important technique for their applications. In this paper, an automatic threshold tracking and change detection algorithm is proposed applicable to high-resolution SAR images. To detect changes within SAR image, a reference image is generated using log-ratio operator and its amplitude distribution is estimated through K-S test. Assuming SAR image has a non-gaussian amplitude distribution, a generalized thresholding technique is applied using Kittler and Illingworth minimum-error estimation. Also, MoLC parametric estimation method is adopted to improve the algorithm performance on rough ground target. The implemented algorithm is tested and verified on the simulated SAR raw data. Then, it is applied to the spaceborne high-resolution SAR images taken by Cosmo-Skymed and KOMPSAT-5 and the performances are analyzed and compared.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.901-912
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• 2022

The frequency of disasters is increasing due to global climate change, and unusual heavy rains and rainy seasons are occurring in Korea. Periodic monitoring and rapid detection are important because these weather conditions can lead to drought and flooding, causing secondary damage. Although research using optical images is continuously being conducted to determine the waterbody, there is a limitation in that it is difficult to detect due to the influence of clouds in order to detect floods that accompany heavy rain. Therefore, there is a need for research using synthetic aperture radar (SAR) that can be observed regardless of day or night in all weather. In this study, using Sentinel-1 SAR images that can be collected in near-real time as open data, the UNet model among deep learning algorithms that have recently been used in various fields was applied. In previous studies, waterbody detection studies using SAR images and deep learning algorithms are being conducted, but only a small number of studies have been conducted in Korea. In this study, to determine the applicability of deep learning of SAR images, UNet and the existing algorithm thresholding method were compared, and five indices and Sentinel-2 normalized difference water index (NDWI) were evaluated. As a result of evaluating the accuracy with intersect of union (IoU), it was confirmed that UNet has high accuracy with 0.894 for UNet and 0.699 for threshold method. Through this study, the applicability of deep learning-based SAR images was confirmed, and if high-resolution SAR images and deep learning algorithms are applied, it is expected that periodic and accurate waterbody change detection will be possible in Korea.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.405-409
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• 2004

We chose the Kushiro wetland in Hokkaido, Japan, as a test site to monitor wetland areas. Synthetic aperture radar (SAR) can carry out continuous observation in any weather conditions, and can therefore be used to observe high humidity areas such as wetlands. We applied multi-parameter SAR data (dual-frequency, multi-polarization, and multi-incidence angle) to monitoring the wetland forest. To find the optimum incidence angle and polarization for monitoring the wetland biomass, a simple backscattering model of wetland vegetation was developed and applied to estimate backscattering coefficients for different biomass and surface conditions.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1058-1060
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• 2003

A ground control point (GCP) is a point on the surface of Earth where image coord inates and map coordinates can be identified. The GCP is useful for the geometric correction of systematic and unsystematic errors usually contained in a remotely sensed data. Especially in case of synthetic aperture radar (SAR) data, it has serious geometric distortions caused by inherent side looking geometry. In addition, SAR images are usually severely corrupted by speckle noises so that it is difficult to identify ground control points. We developed a ground point extraction algorithm that has an improved capability. An application of radargrammetry to Daejon area in Korea was studied to acquire the geometric information. For the ground control point extraction algorithm, an ERS SAR data with precise Delft orbit information and rough digital elevation model (DEM) were used. We analyze the accuracy of the results from our algorithm by using digital map and GPS survey data.

• Proceedings of the KSRS Conference
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• 2000.04a
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• pp.79-84
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• 2000

해양 영역에 대한 SAR(Synthetic Aperture Radar) 자료는 좋은 해상도로 기상조건이나 주야에 상관없이 wind vector를 구할 수 있는 장점이 있다. 해안지역의 scatterometer 자료는 육지의 영향으로 인하여 정확한 자료를 얻을 수 없지만, SAR자료를 이용하면, Scatterometer에 비해 좋은 해상도로 해안지역의 wind vector 추출이 가능하다. 본 연구에서는 SAR 자료로부터 풍속을 추출할 수 있는 CMOD_4와 CMOD_IFR2 알고리즘을 사용하였다. 이 알고리즘들은 정확한 sigma-naught 값과, 풍향, 그리고 local incidence angle을 입력변수로 요구한다. CMOD 알고리즘들은 ERS-1/2와 같이 C-band, VV-polarization을 위해 개발된 알고리즘이므로, C-band, HH-polarization을 가진 RADARSAT 자료에 바로 적용할 수가 없다. 이것을 해결하기 위해 본 연구에서는 두 CMOD 알고리즘을 몇 가지 polarization ratio와 같이 적용하여 보았다. 각 연구지역에 해당하는 자료에는 제주도 주변의 Fine mode 자료, 서해안과 제주도 근해의 Standard mode 자료, 그리고 동해안 지역의 ScanSAR 자료 등이다. 여러 가지 Polarization ratio와 CMOD 알고리즘의 조합, 그리고 2-DFFT로부터 추출된 풍향으로부터 각 연구지역의 풍속은 가까운 기상관측소 및, 부이의 관측값과 비교하였다. 그 결과 Fine mode 자료로부터 추출된 풍속은 실제 관측 값보다 항상 상당히 높게 나타났지만, Standard mode 나 ScanSAR 자료로부터 추출된 풍속은 현지 기상관측소 관측 값과 잘 일치한다.

• Korean Journal of Remote Sensing
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• v.18 no.5
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• pp.273-280
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• 2002

This paper describes a mathematical model and its utilization algorithm for calculating the accurate target position on the ellipsoidal earth surface which corresponds to a range-azimuth coordinates of unprocessed synthetic aperture radar (SAR) images. A geometrical model which is a set of coordinate transformations is described. The side-looking directional angle (off-nadir angle) is determined in an iterative fashion by using the model and the accurate slant range which is calculated from the range sampling timing of the instrument. The algorithm can be applied not only for the geolocation of SAR images but also for the high quality SAR image generation by calculating accurate Doppler parameters.

• Korean Journal of Remote Sensing
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• v.16 no.4
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• pp.355-365
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• 2000

The interferometric correlation, or coherence, is calculated to measure the variance of the interferometric phase and amplitude within the neighbourhood of any location within the image at a result of SAR (Synthetic Aperture Radar) interferometric process which utilizes the phase information of the images. The coherence contains additional information that is useful for detecting point targets which change their location in an area of interest (AOI). In this research, a RGB colour composite image was generated with a intensity image (master image), a intensity change image as a difference between master image and slave image, and a coherence image generated as a part of SAR interferometric processing. We developed a technique performing detection of a point target movement using SAR interferometry and applied it to suitable tandem pair images of ERS-1 and ERS-2 as test data. The possibility of change detection of a point target in the AOI could be identified with the technique proposed in this research.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.434-437
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• 2011

차세대 지능적 무기체계의 자동화를 목표로 SAR(Synthetic Aperture Radar) 영상 신호를 이용한 표적 인식률 향상을 위한 여러가지 방법들이 제안되어 왔다. 기존의 연구들은 SAR 영상의 고차원 특징을 그대로 사용했기 때문에 표적 인식의 성능저하가 있었다. 본 연구에서는 정보 획득 거리가 길고, 날씨에 제약이 없이 전천후 작전 운용이 가능하도록 레이더의 특징과 고해상도 영상을 결합한 SAR 이미지를 이용한 표적 인식률 향상 방법을 제안한다. 효과적인 표적 인식을 하기위해 고차원의 특징벡터를 저차원의 특징벡터로 축소하는 PCA(Principal Component Analysis)를 기반으로 하는 SVM(Support Vector Machine)을 사용한 표적 인식 기법을 사용하였고, PCA 기반의 SVM 분류기를 이용한 표적 인식이 SVM 만을 사용한 표적 인식보다 향상된 성능을 보인 것을 확인하였다.

• Korean Journal of Remote Sensing
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• v.24 no.2
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• pp.99-106
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• 2008

A synthetic aperture radar (SAR) system is of great interest in many fields of civil and military applications because of all-weather and luminance free imaging capability. SAR image quality parameters such as spatial resolution, peak to sidelobe ratio (PSLR), and integrated sidelobe ratio (ISLR) can be normally estimated by modeling of impulse response function (IRF) which is obtained from various system design parameters such as altitude, operational frequency, PRF, etc. In modeling of IRF, however, background clutter environment surrounding the IRF is generally neglected. In this paper, analysis method for SAR mage quality is proposed in the real background clutter environment. First of all, SAR raw data of a point scatterer is generated based on various system parameters. Secondly, the generated raw data can be focused to ideal IRF by range Doppler algorithm (RDA). Finally, background clutter obtained from image of currently operating SAR system is applied to IRF. In addition, image quality is precisely analyzed by zooming and interpolation method for effective extraction of IRF, and then the effect of proposed methodology is presented with several simulation results under the assumption of estimation error of Doppler rate.