• Aerospace Engineering and Technology
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• v.4 no.1
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• pp.114-121
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• 2005

다목적실용위성 2호 영상의 geo-location 정밀도 80 m (CE90) 요구사항을 만족시키기 위하여, 1개의 IRU 와 2개의 star tracker 들로부터 획득되는 데이터를 이용하여 지상에서 후처리 추정 과정을 거쳐 위성의 자세를 결정하는 정밀자세결정 시스템이 개발되었다. 정밀자세결정 시스템의 정밀도를 극대화하기 위해서는 우주 공간의 극심한 열적 환경으로 인해 발생하는 star tracker 정렬 오차를 효율적으로 보정하여야 한다. 정밀한 정렬 오차의 보정을 위해서는 영상 내에 촬영된 지상의 ground control point 데이터를 이용하여야 하는데, 현실적으로 한반도 모든 지역에 대해 ground control point 를 확보할 수 없다. 현재 항공우주연구원이 확보하고 있거나 이후 확보할 예정에 있는 고해상도 영상을 위한 ground control point 들은 대전지역에 국한될 예정이다. 이와 같은 상황에서 정밀자세결정 시스템의 성능을 높이기 위한 최적의 시스템 운용 개념을 본 연구에서 제시하였으며, 시뮬레이션을 통해 그 타당성을 분석하였다.

• Korean Journal of Remote Sensing
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• v.35 no.6_1
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• pp.907-917
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• 2019

Recently, the demand for high-resolution satellite images increases in many fields such as land monitoring and terrain analysis. Therefore, the need for geometric correction is increasing. As an automatic precision geometric correction method, there is a method of automatically extracting the GCP by matching between the GCP Chip and the satellite image. For automatic precision geometric correction, the success rate of matching GCP Chip and satellite image is important. Therefore, it is important to evaluate the matching performance of the manufactured GCP Chip. In order to evaluate the matching performance of GCP Chips, a total of 3,812 GCP Chips in South Korea were used as experimental data. The GCP Chip matching results of KOMPSAT-3A and Google Map showed similar matching results. Therefore, we determined that Google Map satellite imagery could replace high-resolution satellite imagery. Also, presented a method using center point and error radius of Google Map to reduce the time required to verify matching performance. As a result, it is best to set the optimum error radius to 8.5m. Evaluated the matching performance of GCP Chips in South Korea using Google Maps. And verified matching result using presented method. As a result, the GCP Chip s in South Korea had a matching success rate of about 94%. Also, the main matching failure factors were analyzed by matching failure GCP Chips. As a result, Except for GCP Chips that need to be remanufactured, the remaining GCP Chips can be used for the automatic geometric correction of satellite images.

• Korean Journal of Geomatics
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• v.2 no.1
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• pp.31-35
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• 2002

This paper describes an experiment for three-dimensional positioning for a pair of KOMPSAT stereostrips using the ancillary data and a single ground control point. The photogrammetric model for three-dimensional positioning was performed as follows: first, initialization of orbital and attitude parameters derived from ancillary data; second, adjustment of orbital and attitude parameters for the satellite to minimize the ground position error with respect to a GCP using the collinearity condition; third, determination of actual satellite position; and lastly, space intersection. This model was tested for a pair of stereo strips with 0.6 base-to-height ratio and GCPs identified from a 1:5,000 scale digital map. As the result, the satellite position of offset was corrected by only one GCP and the accuracy for the geometric modeling showed 38.89m RMSE.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.567-575
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• 2021

In order to correct the provided RPC and DEM generated from the high-resolution satellite images, the acquisition of the ground control point (GCP) must be preceded. This task is a very complicate that requires field surveys, GPS surveying, and image coordinate reading corresponding to GCPs. In addition, since it is difficult to set up and measure a GCP in areas where access is difficult or impossible (tidal flats, polar regions, volcanic regions, etc.), an alternative method is needed. In this paper, we propose a 3D surface matching technique using only the established ground coordinate package, avoiding the ground-image-location survey of the GCP to correct the DEM produced from WorldView-2 satellite images and the provided RPCs. The location data of the public control points were obtained from the National Geographic Information Institute website, and the DEM was corrected by performing 3D surface matching with this package. The accuracy of 3-axis translation and rotation obtained by the matching was evaluated using pre-measured GPS checkpoints. As a result, it was possible to obtain results within 2 m in the plane location and 1 m in height.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.328-331
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• 2008

It is possible to extract qualified ground control points (GCPs) solely from SAR data without published maps. TerraSAR-X is now in orbit and provides valuable data that have one of the highest spatial resolutions among civilian SAR systems. In this study, a sophisticated method for GCP coordinate extraction from TerraSAR-X stripmap mode data with a 3 m resolution was tested and the quality of the extracted GCPs was evaluated. An inverse-geolocation algorithm was applied to obtain GCPs from TerraSAR-X data. SRTM 90m DEM was used as an auxiliary data set for azimuth time correction of the SAR data. Mean values of the distance errors were 0.11 m and -3.96 m with standard deviations of 6.52 m and 5.11 m in easting and northing, respectively. The result is one of the best among GCPs possibly extracted from current civilian remote sensing systems. The extracted GCPs were used for geo-rectification of an IKONOS image, which demonstrated the applicability of the GCPs to geo-rectification of high resolution optic image. The method used in this study can be applied to KOMPSAT-5 for geo-rectification of high-resolution optic images acquired by KOMPSAT-2 or follow-up missions.

• Journal of the Korean Association of Geographic Information Studies
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• v.1 no.1
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• pp.8-17
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• 1998

This paper presents a method of construction and re-use of a GCP database for precision geometric correction of high resolution satellite images. Accurate geometric correction can be achieved by using accurate GCPs. The GCP information which is extracted from maps or other sources is saved in a database in conjunction with the corresponding image chips. The usage of the GCPs from the database gives reusability and efficiency in marking new GCPs. An image matching algorithm was developed to determine the corresponding positions between an image chip and a new image. The proposed technique can save time in the regular operation of satellite image preprocessing by propagating the pre-determined GCPs to the new image correction.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1135-1147
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• 2021

In order to geometrically correct high-resolution satellite imagery, the sensor modeling process that restores the geometric relationship between the satellite sensor and the ground surface at the image acquisition time is required. In general, high-resolution satellites provide RPC (Rational Polynomial Coefficient) information, but the vendor-provided RPC includes geometric distortion caused by the position and orientation of the satellite sensor. GCP (Ground Control Point) is generally used to correct the RPC errors. The representative method of acquiring GCP is field survey to obtain accurate ground coordinates. However, it is difficult to find the GCP in the satellite image due to the quality of the image, land cover change, relief displacement, etc. By using image maps acquired from various sensors as reference data, it is possible to automate the collection of GCP through the image matching algorithm. In this study, the RPC of KOMPSAT-3A satellite image was corrected through the extracted matching point using the UAV (Unmanned Aerial Vehichle) imagery. We propose a pre-porocessing method for the extraction of matching points between the UAV imagery and KOMPSAT-3A satellite image. To this end, the characteristics of matching points extracted by independently applying the SURF (Speeded-Up Robust Features) and the phase correlation, which are representative feature-based matching method and area-based matching method, respectively, were compared. The RPC adjustment parameters were calculated using the matching points extracted through each algorithm. In order to verify the performance and usability of the proposed method, it was compared with the GCP-based RPC correction result. The GCP-based method showed an improvement of correction accuracy by 2.14 pixels for the sample and 5.43 pixelsfor the line compared to the vendor-provided RPC. In the proposed method using SURF and phase correlation methods, the accuracy of sample was improved by 0.83 pixels and 1.49 pixels, and that of line wasimproved by 4.81 pixels and 5.19 pixels, respectively, compared to the vendor-provided RPC. Through the experimental results, the proposed method using the UAV imagery presented the possibility as an alternative to the GCP-based method for the RPC correction.

• Korean Journal of Remote Sensing
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• v.19 no.6
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• pp.447-456
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• 2003

This study investigated the method for obtaining 3-dimensional terrain information on inaccessable areas by evaluating geometric accuracy of the EOC pass image and scene image acquired from the KOMPSAT-1 satellite. For this purpose, the following four experiments were conducted to evaluate the accuracy of the KOMPSAT-1 EOC satellite data. 1) Calculation of ground coordinates by using ancillary data and image coordinates on Level 1R that were processed by the pre-processing system of KOMPSAT-1. 2) Calculation of 3-dimensional ground coordinates from the ground coordinates of stereo images calculated by using ancillary data, based on space intersections. 3) Execution of bundle adjustment by using GCP (Ground Control Point) extracted in a part of the stereo pass image (KOMPSAT-1 EOC, 1 scene size); and then, evaluation of the ground coordinates from the calculated exterior orientation. 4) Evaluation of accuracy by applying the exterior orientation calculated from 3) To the whole pass image.

• Korean Journal of Remote Sensing
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• v.24 no.4
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• pp.299-307
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• 2008

It is possible to extract qualified ground control points (GCPs) from SAR data itself without published maps. TerraSAR-X data that are one of highest spatial resolution among civilian SAR systems is now available. In this study, a sophisticated method for GCP extraction from TerraSAR-X data was tested and the quality of the extracted GCPs was evaluated. Mean values of the distance errors were 0.11m and -3.96 m with standard deviations of 6.52 m and 5.11 m in easting and northing, respectively. The result is one of the best among GCPs possibly extracted from any civilian remote sensing systems. The extracted GCPs were used for geo-rectification of IKONOS image. The method used in this study can be applied to KOMPSAT-5 for geo-rectification of high-resolution optic images acquired by KOMPSAT-2 or follow-up missions.

• Journal of Korean Society for Geospatial Information Science
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• v.11 no.2 s.25
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• pp.29-37
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• 2003

An approach is described for automatic measuring of GCP's image coordinates from SPOT imagery and focused on the fulfillment an automatic orientation of satellite images. For the orientation of a stereopair of digital images, firstly, GCP(Ground Control Point) should be selected and then the work for measuring of image coordinates correspond to GCPs is required. In this study, we propose the method for extracting the GCP's image coordinates automatically using an image patch for control points and auxiliary points matching. For the evaluation of measurement accuracy, a comparison between points those are extracted manually and automatically by a proposed method have made. Finally, we shows the feasibility of automatic image coordinates measurment by applying in stereo modeling for SPOT images.