• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.04a
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• pp.245-248
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• 2003

본 연구에서는 수치영상처리 방법으로 공간해상도가 l0m인 SPOT 흑백영상을 사용하여, 지상기준점 수량을 10∼30개까지 5개 단위로 증가시키면서 각각의 모델링과 수치표고모델, 정사영상을 생성하여 정사영상상에서 정확도를 평가하였다.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1445-1462
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• 2022

Compact Advanced Satellite 500-4 (CAS500-4), which is scheduled to be launched in 2025, is a mid-resolution satellite with a 5 m resolution developed for wide-area agriculture and forest observation. To utilize satellite images, it is important to establish a precision sensor model and establish accurate geometric information. Previous research reported that a precision sensor model could be automatically established through the process of matching ground control point (GCP) chips and satellite images. Therefore, to improve the geometric accuracy of satellite images, it is necessary to improve the GCP chip matching performance. This paper proposes an improved GCP chip matching scheme for improved precision sensor modeling of mid-resolution satellite images. When using high-resolution GCP chips for matching against mid-resolution satellite images, there are two major issues: handling the resolution difference between GCP chips and satellite images and finding the optimal quantity of GCP chips. To solve these issues, this study compared and analyzed chip matching performances according to various satellite image upsampling factors and various number of chips. RapidEye images with a resolution of 5m were used as mid-resolution satellite images. GCP chips were prepared from aerial orthographic images with a resolution of 0.25 m and satellite orthogonal images with a resolution of 0.5 m. Accuracy analysis was performed using manually extracted reference points. Experiment results show that upsampling factor of two and three significantly improved sensor model accuracy. They also show that the accuracy was maintained with reduced number of GCP chips of around 100. The results of the study confirmed the possibility of applying high-resolution GCP chips for automated precision sensor modeling of mid-resolution satellite images with improved accuracy. It is expected that the results of this study can be used to establish a precise sensor model for CAS500-4.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.4
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• pp.15-23
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• 2004

In order to utilize remote sensed images effectively, it is necessary to correct geometric distortion. Geometric correction is a critical step to remove geometric distortions in satellite images. For geometric correction, Ground Control Points (GCPs) have to be chosen carefully to guarantee the quality of geocoded satellite images, digital maps, GPS surveying or other data. Traditional approach to geometric correction used GCPs requires substantial human operations. Also that is necessary much time and manpower. In this paper, we presented an on-line automatic geometric correction by constructing GCP Chip database. The Proposed on-line automatic geometric correction system is consists of four part. Input image, control the GCP Chip, revision of selected GCP, and output setting part. In conclusion, developed system reduced the processing time and energy for tedious manual geometric correction and promoted usage of Landsat imagery.

• 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.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.3
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• pp.127-137
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• 2018

Recently, UAVs and Drones have been used for various applications. In particular, in the field of surveying, there are studies on the technology for monitoring the terrain based on the high resolution image data obtained by using the UAV-equipped digital camera or various sensors, or for generating high resolution orthoimage, DSM, and DEM. In this study, we analyzed the accuracy of GCP(Ground control point) matching using UAV and VRS-GPS. First, we used VRS-GPS to pre-empt the ground reference point, and then imaged at a base altitude of 150m using UAV. To obtain DSM and orthographic images of 646 images, RMSE was analyzed using pix4d mapper version As a result, even if the number of GCP matches is more than five, the error range of the national basic map(scale : 1/5,000) production work regulations is observed, and it is judged that the digital map revision and gauging work can be utilized sufficiently.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1013-1025
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• 2020

Unmanned Aerial Vehicle (UAV) platform is being widely used in disaster monitoring and smart city, having the advantage of being able to quickly acquire images in small areas at a low cost. Ground Control Points (GCPs) for positioning UAV images are essential to acquire cm-level accuracy when producing UAV-based orthoimages and Digital Surface Model (DSM). However, the on-site acquisition of GCPs takes considerable manpower and time. This research aims to provide an efficient and accurate way to replace the on-site GNSS surveying with three different sources of geospatial data. The three geospatial data used in this study is as follows; 1) 25 cm aerial orthoimages, and Digital Elevation Model (DEM) based on 1:1000 digital topographic map, 2) point cloud data acquired by Mobile Mapping System (MMS), and 3) hybrid point cloud data created by merging MMS data with UAV data. For each dataset a three-dimensional positional accuracy analysis of UAV-based orthoimage and DSM was performed by comparing differences in three-dimensional coordinates of independent check point obtained with those of the RTK-GNSS survey. The result shows the third case, in which MMS data and UAV data combined, to be the most accurate, showing an RMSE accuracy of 8.9 cm in horizontal and 24.5 cm in vertical, respectively. In addition, it has been shown that the distribution of geospatial GCPs has more sensitive on the vertical accuracy than on horizontal accuracy.

• Korean Journal of Remote Sensing
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• v.12 no.1
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• pp.17-25
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• 1996

This paper presents a GCP marking technique using scanned maps. Twelve maps with a scale of 1:250, 000 were scanned and stored as raster images. The distortion factors of scanned maps were modelled by polynomials. The coefficients of the polynomials were determined by a least squre fit. This technique does not require a bulky and expensive digitizing table so that it is suitable for a low-cost pre-processing system. The GCP marking using this technique showed a sufficient accuracy for KITSAT1, 2 narrow camera images.

• 한국지형공간정보학회:학술대회논문집
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• 2002.11a
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• pp.181-186
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• 2002

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.10
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• pp.56-65
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• 1999

This paper presents an architecture and its instruction set for a graphic coprocessor(GCP) which can be used for a multimedia server. The proposed instruction set employs parallel architecture concepts, such as SIMD and Superscalar. GCP consists of a scheduler and four functional units. The scheduler solves an instruction bottleneck problem causing by sharing with four general processors(GPs). GCP can execute up to 4 instructions in parallel. It consists of about 56,000 gates and operates at 30 MHz clock frequency due to speed limitation of SOG technology. GCP meets the real-time DCT algorithm requirement of the CIF image format and can process up to 63 frames/sec for the DCT Algorithm and 21 frames/sec for the Full Block matching Algorithm of the CIF image format.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.11a
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• pp.239-243
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• 2004

Recently various thematic maps and image maps using aerial photograph and satellite imagery are frequently made. The geo-referencing is essential to make image map and topographic map using aerial photograph and satellite imagery. For this geo-referencing, Ground Control Points (GCPs) are needed. In this paper, we used GPS relative positioning to measure GCP ground coordinate and the accuracy of 8cm level was achieved. We made GCP image chips for the efficiency of geo-referencing and carried out the bundle adjustment of aerial photographs using GCP image chips to acquire the GCP photo coordinate with image matching technique. Finally we analyzed the accuracy of bundle adjustment compared to the accuracy of the case in using digital maps to acquire GCP photo coordinate.