• The Journal of Bigdata
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• v.8 no.2
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• pp.177-187
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• 2023

Semi-supervised learning (SSL) is an effective approach to training models using a small amount of labeled data and a larger amount of unlabeled data. However, many papers in the field use a fixed threshold when applying pseudo-labels without considering the feature-wise differences among images of different classes. In this paper, we propose a SSL method for synthetic aperture radar (SAR) image classification that applies different thresholds for each class instead of using a single fixed threshold for all classes. We propose a threshold learning module into the model, considering the differences in feature distributions among classes, to dynamically learn thresholds for each class. We compare the application of a SSL SAR image classification method using different thresholds and examined the advantages of employing class-specific thresholds.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.2
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• pp.183-194
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• 2011

Synthetic aperture radar (SAR) is widely used because of high resolution imaging capability in all weather and day/night condition. In this paper development of Spotlight SAR simulator is proposed for image quality analysis. Proposed SAR simulator is based on the SAR system design parameters so that SAR image performance can be expected which is essential throughout the full system development procedure from the initial concept design stage to the final in-flight calibration and validation stage. The raw data of ideal point target is first generated by taking account of the flight and imaging geometry and the various SAR system design parameters, and the Spotlight image formation algorithm is implemented in order to obtain the point target response. Finally the image quality of the generated raw data is analyzed in terms of spatial resolution, peak to sidelobe ratio and integrated sidelobe ratio.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.16 no.6 s.97
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• pp.615-621
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• 2005

In this paper, a model for simulating synthetic aperture radar(SAR) images of earth surfaces. The earth surfaces include forest area, rice crop field, other agricultural fields, grass field, road, and water surface. At first, the backscattering models are developed for bare soil surfaces, water surfaces, short vegetation fields such as rice fields and grass field, other agriculture areas, and forest areas. Then, the SAR images are generated from the digital elevation model(DEM) and digital terrain map. The DTM includes ten parameters, such as soil moisture, surface roughness, canopy height, leaf width, leaf length, leaf density, branch length, branch density, trunk length, and trunk density, if applicable. The scattering models are verified with measurements, and applied to generate an SAR image for an area.

• Journal of IKEEE
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• v.27 no.3
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• pp.265-272
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• 2023

In this paper, a GPU-accelerated kernel of range Doppler algorithm (RDA) was developed for real-time image formation based on frequency modulated continuous wave (FMCW) synthetic aperture radar (SAR). A pinned memory was used to minimize the data transfer time between the host and the GPU device, and the kernel was configured to perform all RDA operations on the GPU to minimize the number of data transfers. The dataset was obtained through the FMCW drone SAR experiment, and the GPU acceleration effect was measured in an intel i7-9700K CPU, 32GB RAM, and Nvidia RTX 3090 GPU environment. Including the data transfer time between host and devices, it was measured to be accelerated up to 3.41 times compared to the CPU, and when only the acceleration effect of operation was measured without including the data transfer time, it was confirmed that it could be accelerated up to 156 times.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.11A
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• pp.1858-1863
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• 2001

SAR data consist of magnitude and phase, and IFSAR technique using phase data is very useful high technology Producing fee height information. To use IFSAR technique effectively in the operation of SAR, this paper suggests the essential requirement and main consideration during SAR design process. Also the critical baseline, one of the principal elements, is derived, and it proposes applicable method through the simulation and discussion to the E-SAR.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.2
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• pp.187-196
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• 2017

The size of raw data has dramatically increased due to the recent trend of Synthetic Aperture Radar(SAR) development plans for high resolution and high definition image acquisition. The large raw data has an impact on satellite operability due to the limitations of storage and transmission capacity. To improve the SAR operability, the SAR raw data shall be compressed before transmission to the ground station. The Block Adaptive Quantization (BAQ) algorithm is one of the data compression algorithm and has been used for a long time in the spaceborne SAR system. In this paper, an optimization method of BAQ threshold table is introduced using real SAR raw data to prevent the degradation of signal quality caused by data compression. In this manner, a new variation estimation strategy and a new threshold method for block type decision are introduced.

• 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.

• Proceedings of the KSRS Conference
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• 2007.03a
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• pp.61-64
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• 2007

SAR 영상에 존재하는 군사표적은 광학 영상에 있는 군사표적에 비하여 쉽게 구별하기 힘들다. 이는 전체 영상에서 군사표적을 구성하는 픽셀의 수가 매우 적기 때문이다. 이러한 문제 때문에 SAR 영상 분석가들은 영상을 분석하는 것이 어렵다. 이 문제를 해결하기 위해서는 자동화된 분석 시스템이 필요하다. 본 논문에서는 기존에 연구된 SAR 영상을 이용한 자동표적추출 시스템을 분석하고 구현하였다. 구현된 자동표적추출 시스템을 MSTAR 데이터 셋을 이용하여 실험하여 결과를 도출하고， 그 결과를 분석하여 자동표적추출 시스템 각 단계의 성능을 분석하였다. 분석 결과 각 단계별로 최적의 성능을 보여주는 임계값을 알아낼 수 있었다.

• Proceedings of the KSRS Conference
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• 2009.03a
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• pp.147-150
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• 2009

본 논문에서는 해상 클러터를 고려하여 움직이는 물체의 SAR 원시 데이터를 생성하고, SAR 원시 데이터 중간 처리 결과인 range 압축 데이터의 azimuth 차분 신호로부터 물체의 속도를 측정하는 방법을 여러 가지 환경에 적용하여 그 정확도 및 적용 가능한 경우를 분석하였다. 움직이는 물체에 의한 도플러 중심 주파수의 변이가 azimuth 차분 신호에서 위상의 변화를 가져오므로, 이를 이용하여 움직이는 물체의 속도를 측정하는 알고리듬을 정리하였다. 이 알고리듬을 위에서 생성한 range 압축 데이터에 적용하여, 타깃이 되는 물체가 독립적으로 존재하는 경우, azimuth 상에 또 다른 속도를 가지는 산란체가 존재하는 경우, 그리고 높은 후방산란계수를 가지는 육지에 타깃이 되는 물체가 인접해 있는 경우를 가정하여 속도를 측정하였다. 그 결과, 타깃이 되는 물체가 SAR 영상에서 256 픽셀 범위 내에서 독립적으로 존재할 경우에는 높은 정확도로 물체의 속도를 측정할 수 있었으나, 128 픽셀 범위에 다른 움직이는 물체가 존재하거나, 높은 후방산란 계수를 갖는 육지와 인접해 있을 경우에는 최대 1m/s 의 오차를 나타냈다. 이는 주변 산란체의 영향에 의해 신호가 교란되어 목표물의 위치를 추정하는 과정에서 오차가 발생했기 때문이다.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.3
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• pp.475-482
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• 2023

Synthetic Aperture Radar (SAR), which provides images of targets using radio signals, enables monitoring at all times regardless of weather conditions. In this paper, the SAR system was installed on the test aircraft to collect SAR raw data on the ground and the sea, and the results of image formation using the backprojection algorithm were presented.