• 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.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.917-919
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• 2003

This paper demonstrates that RADARSAT ScanSAR data can be an important data source of radar remote sensing for monitoring crop systems and estimation of rice yield for large areas in tropic and sub-tropical regions. Experiments were carried out to show the effectiveness of RADARSAT ScanSAR data for rice yield estimation in whole province of Guangdong, South China. A methodology was developed to deal with a series of issues in extracting rice information from the ScanSAR data, such as topographic influences, levels of agro-management, irregular distribution of paddy fields and different rice cropping systems. A model was provided for rice yield estimation based on the relationship between the backscatter coefficient of multi-temporal SAR data and the biomass of rice.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.1
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• pp.105-113
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• 2010

SAR(Synthetic Aperture Radar) has characteristics well-suited for the measurement of geophysical parameters during day and night in all weather conditions. Recently, SAR data with high resolution acquired by satellites became available to the public. In such data, many features and phenomena of geometric structure of man-made objects and natural environments become observable. In this paper, we discuss main considerations including geometric distortion and coregistration for efficient utilization of high resolution SAR images. And, various advanced technologies in SAR application fields are introduced.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.530-533
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• 2005

A raw signal simulator for synthetic aperture radar (SAR) is a useful tool for the design and implementation of SAR system. Also, in order to analyze and verify the developed SAR processor, the raw signal simulator is required. Moreover, there is the need for a test system to help designing new SAR sensors and mission of SAR system. The derived parameters of the SAR simulator also help to generate accurate SAR processing algorithms. Although the ultimate purpose of this research is to presents a general purpose SAR simulator, this paper presents a SAR simulator in frequency domain at the first step. The proposed simulator generates the raw signal by changing various simulation parameters such as antenna parameters, modulation parameters, and sampling parameters. It also uses the statistics from an actual SAR image to imitate actual physical scattering. This paper introduces the procedures and parameters of the simulator, and presents the simulation results. Experiments have been conducted by comparing the simulated raw data with original raw SAR image. In addition, the simulated raw data have been verified through commercial SAR processing software.

• 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.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.28 no.7
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• pp.580-583
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• 2017

To compensate the degraded SAR image due to the residual errors and the spatial variant errors remaining after the motion compensation in the airborne SAR, we have introduced the autofocus method based on the partition processing. Thus, after we perform the spatial partition for the spotlight SAR data and the time partition for the stripmap SAR data, we reconstruct the subpatch images for the partitioned data. Then, we perform the local autofocus with the suitability analysis process for the phase errors estimated by the autofocus. Moreover, if the estimated phase errors are not properly compensated for the subpatch images, we perform the phase compensation method with the weight to the estimated phase error close to the degraded subpatch image to increase the SAR image quality.

• Journal of Korea Multimedia Society
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• v.25 no.1
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• pp.1-9
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• 2022

Ship detection in synthetic aperture radar (SAR) images is an important application in marine monitoring for the military and civilian domains. Over the past decade, object detection has achieved significant progress with the development of convolutional neural networks (CNNs) and lot of labeled databases. However, due to difficulty in collecting and labeling SAR images, it is still a challenging task to solve SAR ship detection CNNs. To overcome the problem, some methods have employed conventional data augmentation techniques such as flipping, cropping, and affine transformation, but it is insufficient to achieve robust performance to handle a wide variety of types of ships. In this paper, we present a novel and effective approach for deep SAR ship detection, that exploits label-rich Electro-Optical (EO) images. The proposed method consists of two components: a data augmentation network and a ship detection network. First, we train the data augmentation network based on conditional generative adversarial network (cGAN), which aims to generate additional SAR images from EO images. Since it is trained using unpaired EO and SAR images, we impose the cycle-consistency loss to preserve the structural information while translating the characteristics of the images. After training the data augmentation network, we leverage the augmented dataset constituted with real and translated SAR images to train the ship detection network. The experimental results include qualitative evaluation of the translated SAR images and the comparison of detection performance of the networks, trained with non-augmented and augmented dataset, which demonstrates the effectiveness of the proposed framework.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.2
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• pp.171-182
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• 2022

InSAR (Interferometry SAR) technique is a technique that uses complex data to obtain phase difference information from two or more SAR image data, and enables high-resolution image extraction, surface change detection, elevation measurement, and glacial change observation. In many countries, research on the InSAR technique is being conducted in various fields of study such as volcanic activity detection, glacier observation in Antarctica, and ground subsidence analysis. In this study, a case of large ground settlement due to groundwater level drawdown during tunnelling was introduced, and ground settlement analyses using InSAR technique and numerical analysis method were compared. The maximum settlement and influence radius estimated by the InSAR technique and numerical method were found to be quite similar, which confirms the reliability of the InSAR technique. Through this case study, it was found that the InSAR technique reliable to use for estimating ground settlement and can be used as a key technology to identify the long-term ground settlement history in the absence of measurement data.

• Korean Journal of Remote Sensing
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• v.21 no.2
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• pp.163-171
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• 2005

I have developed a processor for synthetic aperture radar (SAR) raw data compression using range-doppler algorithm for educational purpose. The program realized a generic SAR focusing algorithm so that it can deal with any SAR system if the specification is known. It can run efficiently on a low-cost computer by selecting minimum size out of a whole dataset, and can produce intermediate images during the process. Especially, the program is designed for educational purpose in such a way that Doppler centroid and azimuth ambiguity can be determined graphically by the user. By distributing the source code and the algorithm to public, I intend to maximize the educational effect on understanding and utilizing SAR data. This paper introduces the principle of SAR focusing algorithm embedded on the eSAR processor and shows an example of data processing using ERS-1 raw data.

• Korean Journal of Remote Sensing
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• v.27 no.6
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• pp.729-741
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• 2011

Wind speeds have long been estimated from C-band VV-polarized SAR data by using the CMOD algorithms such as CMOD4, CMOD5, and CMOD_IFR2. Some SAR data with HH-polarization without any observations in VV-polarization mode should be converted to VV-polarized value in order to use the previous algorithms based on VV-polarized observation. To satisfy the necessity of polarization ratio (PR) for the conversion, we retrieved the conversion parameter from full-polarized SIR-C SAR image off the east coast of Korea. The polarization ratio for SIR-C SAR data was estimated to 0.47. To assess the accuracy of the polarization ratio coefficient, pseudo VV-polarized normalized radar cross section (NRCS) values were calculated and compared with the original VV-polarized ones. As a result, the estimated psudo values showed a good agreement with the original VV-polarized data with an root mean square error by 0.99 dB. We applied the psudo NRCS to the estimation of wind speeds based on the CMOD wind models. Comparison of the retrieved wind field with the ECMWF and NCEP/NCAR reanalysis wind data showed relatively small rms errors of 1.88 and 1.91 m/s, respectively. SIR-C HH-polarized SAR wind retrievals met the requirement of the scatterometer winds in overall. However, the polarization ratio coefficient revealed dependence on NRCS value, wind speed, and incident angle.