• Journal of the Korea Institute of Military Science and Technology
• /
• v.24 no.4
• /
• pp.392-400
• /
• 2021

Unlike optical equipment, SAR(Synthetic Aperture Radar) has the advantage of obtaining images in all weather, and object detection in SAR images is an important issue. Generally, deep learning-based object detection was mainly performed in real-valued network using only amplitude of SAR image. Since the SAR image is complex data consist of amplitude and phase data, a complex-valued network is required. In this paper, a complex-valued ResNet network is proposed. SAR image object detection was performed by combining the ROI transformer detector specialized for aerial image detection and the proposed complex-valued ResNet. It was confirmed that higher accuracy was obtained in complex-valued network than in existing real-valued network.

• Aerospace Engineering and Technology
• /
• v.10 no.2
• /
• pp.37-48
• /
• 2011

Synthetic Aperture Radar(SAR) is a powerful and well established microwave remote sensing technique which enables high resolution measurement of Earth surface independent of weather conditions and sunlight illumination. KARI has been developing the first Korea SAR satellite which is scheduled to be launched in this year. The SAR satellite mainly consists of the bus platform and SAR payload. Most of all, the RF compatible design during the design phase and the verification of the RF compatibility during the testing phase is very important procedure for the in-orbit performance guarantee because the SAR payload radiates high power through the SAR antenna. In this study, the SAR satellite design criteria and verification procedure for the RF compatibility are described. In addition, this paper describes the RF full radiation testing (RF auto-compatibility testing) for the verification of the RF performance robustness, the testing configuration, and the test results.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.13 no.2
• /
• pp.252-257
• /
• 2010

SAR has pulse compression gain through the process including range and azimuth. Efficient jammers against the SAR with simulated elements are evaluated in the view of power and SAR image. In this paper, J/S is analysed for SAR with RF propagation equation firstly. Several jamming signals on SAR signal are made into SAR image through pulse compression process. Objective jamming performance is evaluated using euclidean distance.

• Korean Journal of Remote Sensing
• /
• v.27 no.1
• /
• pp.25-31
• /
• 2011

Precise control of antenna position is very critical in ArcSAR system which uses an arc-rail as a platform for the antenna movement instead of linear rail. In order to minimize the antenna positional error, we improved the motion driving system and applied a newly developed motion control S/W which utilizes the real time antenna position information from magnetic linear scale and encoder. The experimental results showed that the rotational RMS error was reduced to $0.0062^{\circ}$ from $0.0432^{\circ}$. In terms of antenna positional RMS error for the arm length of 3m, it was reduced to 0.324mm from 2.262mm. It is expected that the ArcSAR system can be used to monitor the sub-millimetric displacement of terrain and structural targets.

• Current Industrial and Technological Trends in Aerospace
• /
• v.4 no.2
• /
• pp.40-48
• /
• 2006

SAR(Synthetic Aperture Radar, 이하 SAR) 위성영상은 광학 영상과는 달리 기상조건의 영향을 거의 받지 않아 대상지역의 주기적인 모니터링이 가능하며, 특정주파수 영역밴드에서는 지표면 투과탐지가 가능하여 재난, 재해, 국방, 환경 분야 등 점차 활용범위가 확대되고 있는 추세이다. 그간의 고해상도 광학위성영상의 기술 개발이 지속적으로 이루어진 상황이라면 그에 비해 영상처리절차가 비교적 까다롭고 복잡한 SAR 영상에 관한 기술개발은 영상 활용의 가치를 가늠해 볼 때 특히 국내 경우 많이 저조한 실정이다. 이와 같은 상황을 고려해 볼 때 현재 개발되고 앞으로 계획단계에 있는 SAR 지구관측 위성의 개발 동향을 파악함으로서 SAR 영상 활용기반구축 마련 및 장기적인 연구개발 계획수립에도 효과적으로 이용될 수 있을 것으로 생각한다.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.25 no.5
• /
• pp.443-461
• /
• 2022

Target Detection in synthetic aperture radar(SAR) image is critical for military and national defense. In this paper, we propose YOLOv4-Attention architecture which adds attention modules to YOLOv4 backbone architecture to complement the feature extraction ability for SAR target detection with high accuracy. For training and testing our framework, we present new SAR embedding datasets based on MSTAR SAR public datasets which are about poor environments for target detection such as various clutter, crowded objects, various object size, close to buildings, and weakness of signal-to-clutter ratio. Experiments show that our Attention YOLOv4 architecture outperforms original YOLOv4 architecture in SAR image target detection tasks in poor environments for target detection.

• Korean Journal of Remote Sensing
• /
• v.10 no.2
• /
• pp.1-15
• /
• 1994

Since Seasat SAR mission in 1978, SAR has become one of the most important surface imaging tools in satellite remote sensing SAR achieves high resolution by signal processing synthesizing a larger aperture. Therefore, SAR signal processing along with antenna technology has been centered upon SAR technologies. Thus interpreters of SAR imagery as well as those who involved in signal processing require the knowledge of the principal SAR processing algorithm. Although the conventional range-Doppler approach has been widely adopted by many SAR processors, azimuth compression including the range migration has been problematic. The recent development of the wavenumber domain approace is able to provide high precision SAR focusing algorithm. Compared with the wavenumber domain algorithm derived by applying Born (first) approximation, the transfer function of the conventional range-Doppler algorithm accounts only for the first order approximation of the exact transfer function. The results of a simulation and an actual test using airborne C-band SAR configuration demonstrate the dxcellent performance of the wavenumber domain algorithm.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.25 no.5
• /
• pp.462-475
• /
• 2022

Different from the field of electro-optical(EO) image analysis, there has been less interest in similarity metrics between synthetic aperture radar(SAR) target images. A reliable and objective similarity analysis for SAR target images is expected to enable the verification of the SAR measurement process or provide the guidelines of target CAD modeling that can be used for simulating realistic SAR target images. For this purpose, this paper presents a similarity analysis method based on the siamese network that quantifies the subjective assessment through the distance learning of similar and dissimilar SAR target image pairs. The proposed method is applied to MSTAR SAR target images of slightly different depression angles and the resultant metrics are compared and analyzed with qualitative evaluation. Since the image similarity is somewhat related to recognition performance, the capacity of the proposed method for target recognition is further checked experimentally with the confusion matrix.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.25 no.1
• /
• pp.30-44
• /
• 2022

The advent of deep learning-based algorithms has facilitated researches on target detection from synthetic aperture radar(SAR) imagery. While most of them concentrate on detection tasks for ships with open SAR ship datasets and for aircraft from SAR scenes of airports, there is relatively scarce researches on the detection of SAR ground vehicle targets where several adverse factors such as high false alarm rates, low signal-to-clutter ratios, and multiple targets in close proximity are predicted to degrade the performances. In this paper, a dataset of ground vehicle targets acquired from TerraSAR-X(TSX) satellite SAR images is presented. Then, both detection and instance segmentation are simultaneously carried out on this dataset based on the deep learning-based Mask R-CNN. Finally, this paper shows the future research directions to further improve the performances of detecting the SAR ground vehicle targets.

• Journal of Space Technology and Applications
• /
• v.1 no.2
• /
• pp.268-281
• /
• 2021

Recently, the synthetic aperture radar (SAR) has been increasingly in demand due to its advantage of being able to observe desired points regardless of time and weather. To utilize SAR data, first of all, many pre-processing such as satellite orbit correction, radiometric calibration, multi-looking, and geocoding are required. For analysis of SAR imagery such as object detection, change detection, and DEM(Digital Elevation Model), additional processings are needed. These pre-processing and additional processes are very complex and require a lot of time and computational resources. In order to handle the SAR images easily, the institutions that use SAR images develop analysis tools and provide users. This paper introduces the function and characteristics of representative SAR imagery analysis tools.