• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.847-855
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• 2018

GMTI (Ground Moving Target Indicator) is a kind of airborne radar function that is used widely in military applications to detect the moving targets on the ground. In this paper, GMTI signal processing technique was presented and its performance was verified using sum and difference channels raw data obtained by the captive flight test.

• Economic and Environmental Geology
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• v.52 no.4
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• pp.313-322
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• 2019

SAR (Synthetic Aperture Radar) remote sensing data is a very useful tool for near-real-time identification of landslide affected areas that can occur over a large area due to heavy rains or typhoons. This study aims to develop an effective algorithm for automatically delineating landslide areas from the polarimetric SAR data acquired after the landslide event. To detect landslides from SAR observations, reduction of the speckle effects in the estimation of polarimetric SAR parameters and the orthorectification of geometric distortions on sloping terrain are essential processing steps. Based on the experimental analysis, it was found that the IDAN filter can provide a better estimation of the polarimetric parameters. In addition, it was appropriate to apply orthorectification process after estimating polarimetric parameters in the slant range domain. Furthermore, it was found that the polarimetric entropy is the most appropriate parameters among various polarimetric parameters. Based on those analyses, we proposed an automatic landslide detection algorithm using the histogram thresholding of the polarimetric parameters with the aid of terrain slope information. The landslide detection algorithm was applied to the ALOS-2 PALSAR-2 data which observed landslide areas in Japan triggered by Typhoon in September 2011. Experimental results showed that the landslide areas were successfully identified by using the proposed algorithm with a detection rate of about 82% and a false alarm rate of about 3%.

• Journal of IKEEE
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• v.22 no.4
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• pp.1012-1018
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• 2018

In this paper, CMOS SAR (Successive Approximation Register) A/D converter with 1.8V supply voltage is designed for IoT sensor processing. This paper proposes design of a 12-bit SAR A/D converter with two A / D converters in parallel to improve the sampling rate. A/D converter1 of the two A/D converters determines all the 12-bit bits, and another A/D converter2 uses the upper six bits of the other A/D converters to minimize power consumption and switching energy. Since the second A/D converter2 does not determine the upper 6 bits, the control circuits and SAR Logic are not needed and the area is minimized. In addition, the switching energy increases as the large capacitor capacity and the large voltage change in the C-DAC, and the second A/D converter does not determine the upper 6 bits, thereby reducing the switching energy. It is also possible to reduce the process variation in the C-DAC by proposed structure by the split capacitor capacity in the C-DAC equals the unit capacitor capacity. The proposed SAR A/D converter was designed using 0.18um CMOS process, and the supply voltage of 1.8V, the conversion speed of 10MS/s, and the Effective Number of Bit (ENOB) of 10.2 bits were measured. The area of core block is $600{\times}900um^2$, the total power consumption is $79.58{\mu}W$, and the FOM (Figure of Merit) is 6.716fJ / step.

• Korean Journal of Remote Sensing
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• v.31 no.2
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• pp.101-110
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• 2015

Synthetic Aperture Radar Interferometry (InSAR) is affected by various noise source such as atmospheric artifact, orbital error, processing noise etc.. Especially, one of the dominant noise source for long-wave SAR system, such as ALOS PALSAR (L-band SAR satellite) is the ionosphere effect because phase delays on radar pulse through the ionosphere are proportional to the radar wavelength. To avoid misinterpret of phase signal in the interferogram, it is necessary to detect and correct ionospheric errors. Recently, a MAI (Multipler Aperture SAR Interferometry) based ionospheric correction method has been proposed and considered one of the effective method to reduce phase errors by ionospheric effect. In this paper, we introduce the MAI-based method for ionospheric correction. Moreover we propose an efficient method that apply the method over non-coherent area using directional filter. Finally, we apply the proposed method to the ALOS PALSAR pairs, which include the west sea coast region in Korea. A polynomial fitting method, which is frequently adopted in InSAR processing, has been applied for the mitigation of phase distortion by the orbital error. However, the interferogram still has low frequency of Sin pattern along the azimuth direction. In contrast, after we applied the proposed method for ionospheric correction, the low frequency pattern is mitigated and the profile results has stable phase variation values within ${\pm}1rad$. Our results show that this method provides a promising way to correct orbital and ionospheric artifact and would be important technique to improve the accuracy and the availability for L-band or P-band systems.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.1143-1153
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• 2018

For TOPS InSAR processing, high-precision image co-registration is required. We propose an image co-registration method suitable for the TOPS mode by comparing the performance of cross correlation method, the geometric co-registration and the enhanced spectral diversity (ESD) matching algorithm based on the spectral diversity (SD) on the Sentinel-1 TOPS mode image. Using 23 pairs of interferometric pairs generated from 25 Sentinel-1 TOPS images, we applied the cross correlation (CC), geometric correction with only orbit information (GC1), geometric correction combined with iterative cross-correlation (GC2, GC3, GC4), and ESD iteration (ESD_GC, ESD_1, ESD_2). The mean of co-registration errors in azimuth direction by cross correlation and geometric matching are 0.0041 pixels and 0.0016 pixels, respectively. Although the ESD method shows the most accurate result with the error of less than 0.0005 pixels, the error of geometric co-registration is reduced to 0.001 pixels by repetition through additional cross correlation matching between the reference and resampled slave image. The ESD method is not applicable when the coherence of the burst overlap areas is low. Therefore, the geometric co-registration method through iterative processing is a suitable alternative for time series analysis using multiple SAR data or generating interferogram with long time intervals.

• Korean Journal of Remote Sensing
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• v.40 no.2
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• pp.229-241
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• 2024

With the advancement of big data processing technology using cloud platforms, access, processing, and analysis of large-volume data such as satellite imagery have recently been significantly improved. In this study, the Change Detection Method, a relatively simple technique for retrieving soil moisture, was applied to the backscattering coefficient values of pre-processed Sentinel-1 synthetic aperture radar (SAR) satellite imagery product based on Google Earth Engine (GEE), one of those platforms, to estimate the surface soil moisture for six observatories within the Yongdam Dam watershed in South Korea for the period of 2015 to 2023, as well as the watershed average. Subsequently, a correlation analysis was conducted between the estimated values and actual measurements, along with an examination of the applicability of GEE. The results revealed that the surface soil moisture estimated for small areas within the soil moisture observatories of the watershed exhibited low correlations ranging from 0.1 to 0.3 for both VH and VV polarizations, likely due to the inherent measurement accuracy of the SAR satellite imagery and variations in data characteristics. However, the surface soil moisture average, which was derived by extracting the average SAR backscattering coefficient values for the entire watershed area and applying moving averages to mitigate data uncertainties and variability, exhibited significantly improved results at the level of 0.5. The results obtained from estimating soil moisture using GEE demonstrate its utility despite limitations in directly conducting desired analyses due to preprocessed SAR data. However, the efficient processing of extensive satellite imagery data allows for the estimation and evaluation of soil moisture over broad ranges, such as long-term watershed averages. This highlights the effectiveness of GEE in handling vast satellite imagery datasets to assess soil moisture. Based on this, it is anticipated that GEE can be effectively utilized to assess long-term variations of soil moisture average in major dam watersheds, in conjunction with soil moisture observation data from various locations across the country in the future.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.1
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• pp.51-58
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• 2015

This paper proposes a 10-b SAR ADC. To increase the conversion speed and reduce the power consumption and area, redundant cycles were implemented digitally in a capacitor DAC. The capacitor DAC algorithm was straightforward switching, which included digital error correction steps. A prototype ADC was implemented in CMOS $0.18-{\mu}m$ technology. This structure consumed $140{\mu}W$ and achieved 59.4-dB SNDR at 1.25MS/s under a 1.8-V supply. The figure of merit (FOM) was 140fJ/conversion-step.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.111-114
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• 2004

Object-based segmentation is the first essential step for image processing applications. Recently, SAR (Synthetic Aperture Radar) segmentation techniques have been developed, however not enough to preserve the significant information contained in the small regions of the images. The proposed method is to partition an SAR image into homogeneous regions by using a fuzzy hit-or-miss operator with an inherent spatial transformation, which endows to preserve the small regions. In our algorithm, an iterative segmentation technique is formulated as a consequential process. Then, each time in iterating, hypothesis testing is used to evaluate the quality of the segmented regions with a homogeneity index. The segmentation algorithm is unsupervised and employed few parameters, most of which can be calculated from the input data. This comparative study indicates that the new iterative segmentation algorithm provides acceptable results as seen in the tested examples of satellite images.

• Proceedings of the Korean Society of Computer Information Conference
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• 2015.01a
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• pp.35-36
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• 2015

무인항공기는 광대역의 전장 정보를 실시간으로 획득하거나 적 위협정보를 파악하기 위해 영상정보를 활용하며 보다 정확한 감시 및 정찰 정보의 획득과 가시화를 위해 정밀한 영상정보처리 기법이 요구된다. 이를 위해 본 논문에서는 무인항공기를 통해 획득한 영상 정보를 지상통제장비로 전송하고 이를 효과적으로 시각화하는 통합 시스템을 제안한다. 제안하는 영상정보처리 시각화 시스템을 통해 무인항공기와 지상통제장비는 전장에서 요구되는 임무와 역할을 효과적으로 수행할 것으로 기대한다.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.346-348
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• 2003

In this study, we attempted to estimate the discharge of the Amazon River branches from JERS-1/SAR images, which are independent of the weather. We visualized some traces of the Amazon River branches, transformed river shapes into a one-dimensional signal, and calculated the characteristics of the river shapes such as the meandering wavelength and the amplitude with Fourier and wavelet analysis. Then, we related the characteristics of the river shapes with the existing discharge data and derived some regression equations. Finally, we estimated the discharge of the Amazon River branches from the SAR images.