• Aerospace Engineering and Technology
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• v.11 no.2
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• pp.164-170
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• 2012

This paper presents that the comparison results of AT (Active Transponder) positions obtained from different measurements: the result of GPS device and evaluated position from the SAR (Synthetic Aperture Radar) image, and active transponders can be useful as GCPs(Ground Control Points) in SAR images. The X-band AT are installed on the wide-and-flat area to improve SCR(signal-to-clutter ration), and activated to represent impulse response function in order to operate as one point target in SAR images. Cosmo-SkyMed operating at X-band frequency are used to provide SAR images of AT. The comparison of AT position is performed by using the result of GPS device field measurement and AT SAR images. ENVI-SARscape S/W is used to evaluate AT position in the SAR images. From the comparison, it is shown that AT are useful as GCPs for SAR images.

• Spatial Information Research
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• v.19 no.1
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• pp.51-59
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• 2011

We produced true ortho images after interpolating occlusion areas and relief displacement of building as well as producing ortho-images to use backward image of ADS which is a aerial digital camera of line type. Also, I was able to produce high quality ortho-images using a small mount of Ground Control Points(GCP) relatively to compare to frame type camera from the evaluation of horizontal position accuracy using ground check points, photo control points for the verification of ortho-images and true-ortho images. Also, I was able to verify the effectiveness in interpolating occlusion areas cause the length overlap was 100% when producing true-ortho images of line type camera.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.387-396
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• 2024

Most very high-resolution (VHR) satellite images provide rational polynomial coefficients (RPC) data to facilitate the transformation between ground coordinates and image coordinates. However, initial RPC often contains geometric errors, necessitating correction through matching with ground control points (GCPs). A GCP chip is a small image patch extracted from an orthorectified image together with height information of the center point, which can be directly used for geometric correction. Many studies have focused on area-based matching methods to accurately align GCP chips with VHR satellite images. In cases with seasonal differences or changed areas, edge-based algorithms are often used for matching due to the difficulty of relying solely on pixel values. However, traditional edge extraction algorithms,such as canny edge detectors, require appropriate threshold settings tailored to the spectral characteristics of satellite images. Therefore, this study utilizes deep learning-based edge information that is insensitive to the regional characteristics of satellite images for matching. Specifically,we use a pretrained pixel difference network (PiDiNet) to generate the edge maps for both satellite images and GCP chips. These edge maps are then used as input for normalized cross-correlation (NCC) and relative edge cross-correlation (RECC) to identify the peak points with the highest correlation between the two edge maps. To remove mismatched pairs and thus obtain the bias-compensated RPC, we iteratively apply the data snooping. Finally, we compare the results qualitatively and quantitatively with those obtained from traditional NCC and RECC methods. The PiDiNet network approach achieved high matching accuracy with root mean square error (RMSE) values ranging from 0.3 to 0.9 pixels. However, the PiDiNet-generated edges were thicker compared to those from the canny method, leading to slightly lower registration accuracy in some images. Nevertheless, PiDiNet consistently produced characteristic edge information, allowing for successful matching even in challenging regions. This study demonstrates that improving the robustness of edge-based registration methods can facilitate effective registration across diverse regions.

• Korean Journal of Remote Sensing
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• v.19 no.4
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• pp.285-290
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• 2003

In this paper, a camera modeling method that utilizes ephemeris data and imaging geometry is presented. The proposed method constructs a mathematical model only with parameters that are contained in auxiliary files and does not require any ground control points for model construction. Control points are only needed to eliminate geolocation error of the model that is originated from errors embedded in the parameters that are used in model construction. By using a few (one or two) control points, RMS error of around pixel size can be obtained and control points are not necessarily uniformly distributed in line direction of the scene. This advantage is crucial in large-scale projects and will enable to reduce project cost dramatically.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.11 no.4
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• pp.163-170
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• 1991

The 3 dimensional point positioning from SPOT imagery is performed by bundle adjustment methods of analytical and digital photogrammetry, and need the precise determination of image coordinates and accurate coordinates of ground control points. In this study, the authors analysed the digitized planimetric accuarcy and height accuracy of topographic maps in comparison with accurate coordinates by coordinates resulted by bundle adjustment in each cases between different acquisition method of ground control point coordinates and formats of SPOT imagery.

• Spatial Information Research
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• v.13 no.4 s.35
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• pp.373-380
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• 2005

Mobile Mapping Systems are effective systems to acquire the position and image data using vehicle equipped with the GPS (Global Positioning System), IMU (Inertial Measurement Unit), and CCD camera. They are used in various fields of road facility management, map update, and etc. In the general photogrammetry such as aerial photogrammetry, GCP (Ground Control Point)s are needed to compute the image exterior orientation elements (the position and attitude of camera). These points are measured by field survey at the time of data acquisition. But it costs much time and money. Moreover, it is not possible to make sufficient GCP as much as we want. However Mobile Mapping Systems are more efficient both in time and money because they can obtain the position and attitude of camera at the time of photographing. That is, Image Orientation Technique must use GCP to compute the image exterior orientation elements, but on the other hand Direct Georeferencing can directly compute the image exterior orientation elements by GPS/INS. In this paper, we analyze about the positional accuracy comparison of ground point using the Image Orientation Technique and Direct Georeferencing Technique.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.2
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• pp.203-211
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• 2009

With supply of the domestic digital camera, the relative importance of the digital camera is coming to be high gradually on aerial photogrammetry, the image of digital camera is more applied in image map or digital topographic map production. But, there are cases that do not have position information or attitude information of each photograph in digital camera results. Therefore, we wish to present additional method to get more accurate photograph control point result. In this study, One is called A method, which is the case of entering positioning information of principal point from topographic map as default values that are need to extract tie point automatically using by 56 pieces of photography that are photographed by DMC to the extent to 5 courses and 35 GCP points. The other is called B-method, which is the case of entering exterior orientation parameters that are processed by block adjustment for A-method using by 4 control points in method-1 as default values. We have analyzed about results per control points arrangement for two cases using MATCH-AT that is photograph control point measurement S/W of Germany INPHO company. As a result of analysis, accuracy of B-method was better than that of A-method, and we could get more accurate results if block adjustments are executed including self calibration. Also, it is more effective in expense side that using self calibration for photograph survey in B-method because can reduce GCP numbers.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.287-291
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• 2006

Currently application of high-resolution satellite imagery is expanding with development of high tech optical and space aviation technology. Although using 3 dimensional modeling technology in order to attain accurate terrain information using existing ground control points is the most dependable reference data, such means are unapplicable for certain area because of it's limited access. In this study, we have researched into ways to utilizing high resolution satellite images from IKONOS and Quickbird, and sub-meter class satellites images that will be utilized In the future such as Arirang images and PLEIADES images for unaccessible areas. For that purpose we have created accuracy verification and GCP files for existing ortho-imagery and digital elevation model. The results showed that accuracy of ortho-Imagery and digital elevation model was RMSE X:3.043m, Y:2.921m, Z:6.139m. Also, after ortho-rectifying IKONOS images using ground control points extracted from ortho imagery and digital elevation model the accuracy of the imagery was RMSE X:3.243m, Y:2.067m, Z:1.872m.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.04a
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• pp.15-26
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• 2004

The calibration for systematic error in LiDAR is crucial for the accuracy of airborne laser scanning. The main error is the misalignment of platforms between INS(Inertial Navigation System) and Laser scanner For planimetrical calibration of LiDAR, the building is good feature which has great changes in height and continuous flat area in the top. The planimetry error(pitch, roll) is corrected by adjustment of height which is calculated from comparing ground control points(GCP) of building to laser scanning data. We can know scale correction of laser range by the comparison of LiDAR data and GCP is arranged at the end of scan angle where maximize the height error. The area for scale calibration have to be large flat and have almost same elevation. At 1000m for average flying height, The Accuracy of laser scanning data using LiDAR is within 110cm in height and ${\pm}$50cm in planmetry so we can use laser scanning data for generating 3D terrain surface, expecically digital surface model(DSM) which is difficult to measure by aerial photogrammetry in forest, coast, urban area of high buildings

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.667-670
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• 2005

Ground control points(GCPs) can be extracted from SAR data given precise orbit for DTM generation using optic images and other SAR data. In this study, we extract GCPs from ERS SAR data and SRTM DEM. Although it is very difficult to identify GCPs in ERS SAR image, the geometry of optic image and other SAR data are able to be corrected and more precise DTM can be constructed from stereo optic images. Twenty GCPs were obtained from the ERS SAR data with precise Delft orbit information. After the correction was applied, the mean values of planimetric distance errors of the GCPs were 3.7m, 12.1 and -0.8m with standard deviations of 19.9m, 18.1, and 7.8m in geocentric X, Y, and Z coordinates, respectively. The geometries of SPOT stereo pair were corrected by 13 GCPs, and r.m.s. errors were 405m, 705m and 8.6m in northing, easting and height direction, respectively. And the geometries of RADARS AT stereo pair were corrected by 12 GCPs, and r.m.s. errors were 804m, 7.9m and 6.9m in northing, easting and height direction, respectively. DTMs, through a method of area based matching with pyramid images, were generated by SPOT stereo images and RADARS AT stereo images. Comparison between points of the obtained DTMs and points estimated from a national 1 :5,000 digital map was performed. For DTM by SPOT stereo images, the mean values of distance errors in northing, easting and height direction were respectively -7.6m, 9.6m and -3.1m with standard deviations of 9.1m, 12.0m and 9.1m. For DTM by RADARSAT stereo images, the mean values of distance errors in northing, easting and height direction were respectively -7.6m, 9.6m and -3.1m with standard deviations of 9.1m, 12.0m and 9.1m. These results met the accuracy of DTED level 2