• Korean Journal of Remote Sensing
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• v.15 no.1
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• pp.31-38
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• 1999

In this paper, we introduce an algorithm for the ortho-rectification of high resolution pushbroom-type satellite images. The generation of ortho-images in the ultimate level of the satellite image preprocessing which also includes systematic geocoding and precision geocoding. It is also essential for the mapping of satellite images because topotraphic maps are based on the orthographic projection. The newly developed ortho-image generation algorithm introduced in this paper is on the line of the algorithms previously developed (Shin and Lee, 1997; Shin e 1998). Various experimental results are shown in this paper. The results show that the algorithm completely eliminates the disparities in the perspectively viewed images which were caused by the terrain height. The absolute accuracy of the developed algorithm depends on the accuracy of the camera model and the digital elevation model used.

• Korean Journal of Remote Sensing
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• v.20 no.2
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• pp.125-137
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• 2004

In case of using image sequence taken from a moving camera along a road in an urban area, general video mosaicing technique based on a single baseline cannot create 2-D image mosaics. To solve the drawback, this paper proposed a new image mosaicing technique through 3-D multi-baselines that can create image mosaics in 3-D space. The core of the proposed method is that each image frame has a dependent baseline, an equation of first order, calculated by using ground control point (GCP) of optical flows. The proposed algorithm consists of 4 steps: calculation of optical flows using hierarchical strategy, calculation of camera exterior orientation, determination of multi-baselines, and seamless image mosaics. This paper realized and showed the proposed algorithm that can create efficient image mosaics in 3-D space from real image sequence.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.847-860
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• 2020

The 2-pass DInSAR (Differential Interferometric SAR) processing steps for DEM generation consist of the co-registration of SAR image pair, interferogram generation, phase unwrapping, calculation of DEM errors, and geocoding, etc. It requires complicated steps, and the accuracy of data processing at each step affects the performance of the finally generated DEM. In this study, we developed an improved method for enhancing the performance of the DEM generation method based on the 2-pass DInSAR technique of TanDEM-X bistatic SAR images was developed. The developed DEM generation method is a method that can significantly reduce both the DEM error in the unwrapped phase image and that may occur during geocoding step. The performance analysis of the developed algorithm was performed by comparing the vertical accuracy (Root Mean Square Error, RMSE) between the existing method and the newly proposed method using the ground control point (GCP) generated from GPS survey. The vertical accuracy of the DInSAR-based DEM generated without correction for the unwrapped phase error and geocoding error is 39.617 m. However, the vertical accuracy of the DEM generated through the proposed method is 2.346 m. It was confirmed that the DEM accuracy was improved through the proposed correction method. Through the proposed 2-pass DInSAR-based DEM generation method, the SRTM DEM error observed by DInSAR was compensated for the SRTM 30 m DEM (vertical accuracy 5.567 m) used as a reference. Through this, it was possible to finally create a DEM with improved spatial resolution of about 5 times and vertical accuracy of about 2.4 times. In addition, the spatial resolution of the DEM generated through the proposed method was matched with the SRTM 30 m DEM and the TanDEM-X 90m DEM, and the vertical accuracy was compared. As a result, it was confirmed that the vertical accuracy was improved by about 1.7 and 1.6 times, respectively, and more accurate DEM generation was possible with the proposed method. If the method derived in this study is used to continuously update the DEM for regions with frequent morphological changes, it will be possible to update the DEM effectively in a short time at low cost.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.33-33
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• 2021

예측하기 어려운 복잡한 기후 변화로 인해 수자원 관리측면에서 다양한 방법을 도입하여 해결할 수 있는 방안이 국가적 주요 관심사로 다루어지고 있다. 따라서 투입인력과 소요시간 절감, 장비와 인력진입 불가지역에 대한 정보획득, 높은 공간해상도, 항공측량 대비 높은 경제성 등 다양한 장점의 드론을 이용한 하천지형 특성별 수리특성 분석방안이 필요하다. 본 연구에서는 성연천 하류부지역을 대상으로 위성항법시스템(Global Navigation Satellite System, GNSS) 측량 지형성과와 드론측량(Drone) 지형성과를 지상에 설치된 CHP(Check Point) 좌표 값을 확인하여 두 지형의 정확도를 비교하였으며 HEC-RAS 모형을 이용하여 빈도별 수리특성을 비교 산정하였다. 본 연구는 성연천 하류 480m구간을 선정하고 GNSS를 이용한 실측지형자료와 GCP(Ground Control Point)를 얻기 위해 정확도 검증을 실시하였으며 위성항법시스템(GNSS) 측량과 DRONE RGB측량의 CHP(Check Point) 오차를 비교하여 정확도를 검증하였다. 오차 값이 확인된 위성항법시스템(GNSS)을 이용하여 가상기준점을 선정하고 RTK 모바일스테이션을 설치하여 DRONE LIDAR측량을 통해 지형자료를 취득하였으며 얻어진 지형자료를 HEC-RAS를 통해 입력 후 성연천 하천기본계획에 제시되어진 조도계수와 빈도별 홍수위를 적용하여 연구구간 480m에 대해 100년 빈도의 결과 값을 비교 검토하였다. 100년 빈도 계획 홍수량 조건의 하상과 한계수위의 차에서 위성항법시스템(GNSS) 측량 지형자료를 기준으로 평균수위 측정오차는 드론 RGB 측량 지형자료 0.460m, 드론 LIDAR 측량 지형자료 0.260m의 결과를 얻었으며 동일 조건 흐름하의 평균유속에서 위성항법시스템(GNSS) 측량 지형자료를 기준으로 평균유속 측정오차는 드론 RGB 측량 지형자료 0.40m/s, 드론 LIDAR 측량 지형자료 0.36m/s의 결과를 얻었다. 통수 단면적의 비교 결과는 위성항법시스템(GNSS) 측량 지형자료를 기준으로 드론 RGB 측량 지형자료 전체 단면의 평균오차는 20.20m², 드론 LIDAR 측량 지형자료 전체 단면의 평균오차는 21.68²의 결과를 얻었으며 이상에서와 같이 홍수위와 평균유속, 통수 단면적의 측정오차 비교 결과를 종합할 때 통수 단면적 측정결과는 위성항법시스템(GNSS) 측량과 드론 RGB 측량의 차이가 적었으나 계획 홍수량 조건의 하상과 한계수위 차이와 동일조건 흐름하의 평균유속에서 위성항법시스템(GNSS) 측량과 드론 LIDAR 측량의 차이가 적은 것으로 나타났다. 그리고 통수용량(capacity)(m³) 비교에서는 위성항법시스템(GNSS) 측량을 기준으로 드론 RGB 측량은 약 7644m³, 드론 LIDAR 측량은 약 7547m³의 차이를 보여 드론 LIDAR를 이용한 결과가 가장 정확한 측정방법으로 추천할 수 있음을 확인하였다.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1615-1627
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• 2020

The purpose of this study is to compare and analyze the performance of KOMPSAT-3 Digital Surface Model (DSM) made in overseas testbed area. To that end, we collected the KOMPSAT-3 in-track stereo image taken in San Francisco, the U.S. The stereo geometry elements (B/H, converse angle, etc.) of the stereo image taken were all found to be in the stable range. By applying precise sensor modeling using Ground Control Point (GCP) and DSM automatic generation technique, DSM with 1 m resolution was produced. Reference materials for evaluation and calibration are ground points with accuracy within 0.01 m from Compass Data Inc., 1 m resolution Elevation 1-DSM produced by Airbus. The precision sensor modeling accuracy of KOMPSAT-3 was within 0.5 m (RMSE) in horizontal and vertical directions. When the difference map was written between the generated DSM and the reference DSM, the mean and standard deviation were 0.61 m and 5.25 m respectively, but in some areas, they showed a large difference of more than 100 m. These areas appeared mainly in closed areas where high-rise buildings were concentrated. If KOMPSAT-3 tri-stereo images are used and various post-processing techniques are developed, it will be possible to produce DSM with more improved quality.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.4_1
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• pp.299-309
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• 2014

This paper evaluated the possibility for 1/5,000 digital topographic mapping by using PLEIADES images of 0.5m GSD(Ground Sampling Distance) resolution that has recently launched. Those results of check points by applying the initial RPC(Rational Polynomial Coefficient) of PLEIADES images came out as; RMSE of those were $X={\pm}1.806m$, $Y={\pm}2.132m$, $Z={\pm}1.973m$. Also, if we corrected geometric correction using 16 GCP(Ground Control Point)s, the results of RMSE became $X={\pm}0.104m$, $Y={\pm}0.171m$, $Z={\pm}0.036m$, and t he RMSE of check points were $X={\pm}0.357m$, $Y={\pm}0.239m$, $Z={\pm}0.188m$; which of those results indicated the accuracy of standard adjustment complied in error tolerances of the 1/5,000 scale. Additionally, we converted coordinates of points, obtained by TerraSAR. for comparing with measurements from GPS(Global Positioning System) surveying. The RMSE of comparing converted and GPS points were $X={\pm}0.818m$, $Y={\pm}0.200m$, $Z={\pm}0.265m$, which confirmed the possibility for 1/5,000 digital topographic mapping with PLEIADES images and GCPs. As method of obtaining GCPs in unaccessible area, however, the outcome evaluation of GCPs extracted from TerraSAR images was not acceptable for 1/5,000 digital topographic mapping. Therefore, we considered that further researches are needed on applicability of GCPs extracted from TerraSAR images for future alternative method.