• Korean Journal of Remote Sensing
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• v.28 no.2
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• pp.227-235
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• 2012

The InSAR (Interferometric SAR) processing steps for DEM generation consist of the coregistration of two SAR data, interferogram generation, phase filtering, phase unwrapping, phase to height conversion, and geocoding, etc. In this study, we developed the precise algorithm for phase to height conversion, including the ambiguity method taking into account Earth ellipsoid, Schw$\ddot{a}$visch method, and the refined ambiguity method suitable for the interferometric pair with non-parallel obit. From the testing with JERS-1 orbit we found that the height error by traditional ambiguity method reaches to about 40 m during phase to height conversion. The proposed methods are very useful in generating precise InSAR DEM;especially in the case of using non-parallel InSAR pair due to unstable orbit control such as JERS-1 or intentional orbit control such as Cross-InSAR pair between ERS2 and ENVISAT satellite.

• Journal of Korea Water Resources Association
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• v.51 no.12
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• pp.1181-1194
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• 2018

This study suggests a novel approach of estimating stream flow discharge using the Synthetic Aperture Radar (SAR) images taken from 2015 to 2017 by European Space Agency Sentinel-1 satellite. Fifteen small to medium sized rivers in the Han River basin were selected as study area, and the SAR satellite images and flow data from water level and flow observation system operated by the Korea Institute of Hydrological Survey were used for model construction. First, we apply the histogram matching technique to 12 SAR images that have undergone various preprocessing processes for error correction to make the brightness distribution of the images the same. Then, the flow estimation model was constructed by deriving the relationship between the area of the stream water body extracted using the threshold classification method and the in-situ flow data. As a result, we could construct a power function type flow estimation model at the fourteen study areas except for one station. The minimum, the mean, and the maximum coefficient of determination ($R^2$) of the models of at fourteen study areas were 0.30, 0.80, and 0.99, respectively.

• Bulletin of the Korean Space Science Society
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• 2008.04a
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• pp.29.3-29.3
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• 2008

• Bulletin of the Korean Space Science Society
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• 1997.04a
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• pp.32.1-32
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• 1997

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1095-1107
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• 2020

Flood monitoring using satellite data has been constrained by obtaining satellite images for flood peak and accurately extracting flooded areas from satellite data. Deep learning is a promising method for satellite image classification, yet the potential of deep learning-based flooded area extraction using SAR data remained uncertain, which has advantages in obtaining data, comparing to optical satellite data. This research explores the performance of SegNet and U-Net on image segmentation by extracting flooded areas in the Khorat basin, Mekong river basin, and Cagayan river basin in Thailand, Laos, and the Philippines from Sentinel-1 A/B satellite data. Results show that Global Accuracy, Mean IoU, and Mean BF Score of SegNet are 0.9847, 0.6016, and 0.6467 respectively, whereas those of U-Net are 0.9937, 0.7022, 0.7125. Visual interpretation shows that the classification accuracy of U-Net is higher than SegNet, but overall processing time of SegNet is around three times faster than that of U-Net. It is anticipated that the results of this research could be used when developing deep learning-based flood monitoring models and presenting fully automated flooded area extraction models.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.04a
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• pp.277-281
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• 2003

We fused RADARSAT image and SPOT panchromatic image by wavelet transform in order to improve the accuracy of classification on the water area. Fused image in water not only maintained the characteristic of SAR image (low pixel value)but also had boundary information improved. This leads to accurate method to classify water areas.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.5
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• pp.169-173
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• 2019

In this paper, SAR payload operation mode design method, results and performance analysis results are suggested. To SAR payload design, pramary parameter should be identified and designed. It is designed considering the small satellite of less than 100kg operated in low earth orbit. Also, an antenna structure for small size and light weight is proposed. Performance analysis is performed by applying the design values.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.385-388
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• 2018

군남댐의 운영은 북한지역에 위치한 저수지와 하천 상황, 특히 황강댐 방류에 절대적으로 의존하는 특성이 있음에도 불구하고, 수위 상황 등 자료수집의 한계로 군남댐 운영에 어려움이 많다. 이러한 상황에서 위성원격탐사 영상자료는 미계측 북한 접경지역의 수문상황을 판단하는 데 유용한 자료가 될 수 있다. 위성을 통한 수위 추정 방법은 위성영상에서 탐지된 수표면을 DEM과 중첩하여 판독하는 방법인 imaging 기법과 레이더고도계로 불리는 altimeter로 위성에서 수표면까지의 거리를 직접 측정하여 산출하는 profiling 기법 등 크게 두 가지 방법이 있다. 본 연구에서는 위성영상으로 산출된 DEM과 ESA의 Sentinel-1 C-밴드 SAR 영상을 중첩하여 황강댐 수위를 추정해 보았다. 정확도 문제가 있겠지만, 황강댐 수위 변화의 경향성은 확인할 수 있었으므로, 향후 개선을 통해 황강댐 수위변동 추세 분석과 상황별 적절한 사전 대응에 활용할 수 있을 것으로 판단된다.

• Korean Journal of Remote Sensing
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• v.26 no.2
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• pp.109-114
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• 2010

This paper studies the origin of low back-scattering area appeared on the SAR image taken on the coastal waters off the southeast coast of Korea on July 5, 2000. Cold waters were frequently observed during summer on this coastal waters, and quasi-simultaneously taken AVHRR and SeaWiFS images also showed cold surface waters and high concentration of chlorophyll-a, respectively. By synergetic analysis of multi-sensor satellite images, it is strongly suggested that the cold and nutrient rich upwelling waters caused the high phytoplankton density and high biological activities in the water producing natural films for low back-scattering.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.847-859
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• 2021

Recently, as the number of high-resolution satellite SAR images increases, the demand for precise matching of SAR imagesin change detection and image fusion is consistently increasing. RMSE (Root Mean Square Error) values using GCPs (Ground Control Points) selected by analysts have been widely used for quantitative evaluation of image registration results, while it is difficult to find an approach for automatically measuring the registration accuracy. In this study, a feasibility analysis was conducted on using the FSIM (Feature Similarity) index as a measure to evaluate the registration accuracy. TerraSAR-X (TSX) staring spotlight data collected from various incidence angles and orbit directions were used for the analysis. FSIM was almost independent on the spatial resolution of the SAR image. Using a single SAR image, the FSIM with respect to registration errors was analyzed, then use it to compare with the value estimated from TSX data with different imaging geometry. FSIM index slightly decreased due to the differencesin imaging geometry such as different look angles, different orbit tracks. As the result of analyzing the FSIM value by land cover type, the change in the FSIM index according to the co-registration error was most evident in the urban area. Therefore, the FSIM index calculated in the urban was mostsuitable for determining the accuracy of image registration. It islikely that the FSIM index has sufficient potential to be used as an index for the co-registration accuracy of SAR image.