• Korean Journal of Geomatics
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• v.4 no.1
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• pp.31-37
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• 2004

The aim of this research is comparing the existing approximation models (e.g. Affine Transformation and Direct Linear Transformation) with Rational Function Model as a substitute of rigorous sensor model of linear array scanner, especially push-broom sensor. To do so, this research investigates the mathematical model of each approximation method. This is followed by the assessments of accuracy of transformation from object space to image space by using simulated data generated by collinearity equations which incorporate or depict the physical aspects of linear array sensor.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.1 no.2
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• pp.23-29
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• 1983

Degradation of image caused by relative motion between the object and the imaging system (like a camera with its platform) is detrimental to precision photogrammetry. Principal modes of relative motion are identified. The discussion is, however, concentrated on the systematic motions, translatory and rotatory. Various analogical approaches of compensating for the image motion are cited. An analytical-computational approach is presented. This one considers the relationship of transformation bet ween the image and the object, known as the collinearity condition. The standard forms of collinearity condition equations are presented. Augmentation of these equations with regard to both translatory and rotatory motions are expounded. With ever increasing use of high speed computers (as well as analytical plotters in the realm of photogrammetry), this approach seems to be more costeffective and seems to yield better precision in the long run than other approaches that concentrate on analogical corrections to the image itself.

• Korean Journal of Remote Sensing
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• v.24 no.3
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• pp.223-233
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• 2008

In the mid 90's, the U.S. government released images acquired by the first generation of photo reconnaissance satellite missions between 1960 and 1972. The Declassified Intelligent Satellite Photographs (DISP) from the Corona mission are of high quality with an astounding ground resolution of about 2 m. The KH-4A panoramic camera system employed a scan angle of $70^{\circ}$ that produces film strips with a dimension of $55\;mm\;{\times}\;757\;mm$. Since GPS/INS did not exist at the time of data acquisition, the exterior orientation must be established in the traditional way by using control information and the interior orientation of the camera. Detailed information about the camera is not available, however. For reconstructing points in object space from DISP imagery to an accuracy that is comparable to high resolution (a few meters), a precise camera model is essential. This paper is concerned with the derivation of a rigorous mathematical model for the KH-4A/B panoramic camera. The proposed model is compared with generic sensor models, such as affine transformation and rational functions. The paper concludes with experimental results concerning the precision of reconstructed points in object space. The rigorous mathematical panoramic camera model for the KH-4A camera system is based on extended collinearity equations assuming that the satellite trajectory during one scan is smooth and the attitude remains unchanged. As a result, the collinearity equations express the perspective center as a function of the scan time. With the known satellite velocity this will translate into a shift along-track. Therefore, the exterior orientation contains seven parameters to be estimated. The reconstruction of object points can now be performed with the exterior orientation parameters, either by intersecting bundle rays with a known surface or by using the stereoscopic KH-4A arrangement with fore and aft cameras mounted an angle of $30^{\circ}$.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.2
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• pp.211-221
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• 2012

To utilize omni-directional images acquired by a rotating line camera for indoor spatial information services, we should register precisely the images with respect to an indoor coordinate system. In this study, we thus develop a georeferencing method to estimate the exterior orientation parameters of an omni-directional image - the position and attitude of the camera at the acquisition time. First, we derive the collinearity equations for the omni-directional image by geometrically modeling the rotating line camera. We then estimate the exterior orientation parameters using the collinearity equations with indoor control points. The experimental results from the application to real data indicate that the exterior orientation parameters is estimated with the precision of 1.4 mm and $0.05^{\circ}$ for the position and attitude, respectively. The residuals are within 3 and 10 pixels in horizontal and vertical directions, respectively. Particularly, the residuals in the vertical direction retain systematic errors mainly due to the lens distortion, which should be eliminated through a camera calibration process. Using omni-directional images georeferenced precisely with the proposed method, we can generate high resolution indoor 3D models and sophisticated augmented reality services based on the models.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.8
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• pp.673-679
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• 2013

Photogrammetric techniques were used to analyze separation characteristics of store in safe separation flight test. In this analysis, we used single camera method to analyze 6 degrees of freedom of separated store. We established instrumentation requirements based on theoretical background of photogrammetry and guidelines of MIL-HDBK-1763. We applied the photogrammetry to flight test and extracted separation trajectory. We empirically estimated the input variables uncertainties of photogrammetry and its effects on separation trajectory. Using this trajectory which includes the photogrammetric error, we analyzed the safety of separation.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.04a
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• pp.471-477
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• 2003

This paper presents an algorithm that automatically extracts buildings among many different features on the earth surface by fusing LIDAR data with panchromatic aerial images. The proposed algorithm consists of three stages such as point level process, polygon level process, parameter space level process. At the first stage, we eliminate gross errors and apply a local maxima filter to detect building candidate points from the raw laser scanning data. After then, a grouping procedure is performed for segmenting raw LIDAR data and the segmented LIDAR data is polygonized by the encasing polygon algorithm developed in the research. At the second stage, we eliminate non-building polygons using several constraints such as area and circularity. At the last stage, all the polygons generated at the second stage are projected onto the aerial stereo images through collinearity condition equations. Finally, we fuse the projected encasing polygons with edges detected by image processing for refining the building segments. The experimental results showed that the RMSEs of building corners in X, Y and Z were ${\pm}$8.1cm, ${\pm}$24.7cm, ${\pm}$35.9cm, respectively.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.241-243
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• 2003

This paper presents an algorithm that automatically extracts buildings among many different features on the earth surface by fusing LIDAR data with panchromatic aerial images. The proposed algorithm consists of three stages such as point level process, polygon level process, parameter space level process. At the first stage, we eliminate gross errors and apply a local maxima filter to detect building candidate points from the raw laser scanning data. After then, a grouping procedure is performed for segmenting raw LIDAR data and the segmented LIDAR data is polygonized by the encasing polygon algorithm developed in the research. At the second stage, we eliminate non-building polygons using several constraints such as area and circularity. At the last stage, all the polygons generated at the second stage are projected onto the aerial stereo images through collinearity condition equations. Finally, we fuse the projected encasing polygons with edges detected by image processing for refining the building segments. The experimental results showed that the RMSEs of building corners in X, Y and Z were ${\pm}$8.1㎝, ${\pm}$24.7㎝, ${\pm}$35.9㎝, respectively.

• Journal of the Korean Data and Information Science Society
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• v.24 no.2
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• pp.267-275
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• 2013

Among data mining techniques, the association rule is the most recently developed technique, and it finds the relevance between two items in a large database. And it is directly applied in the field because it clearly quantifies the relationship between two or more items. When we determine whether an association rule is meaningful, we utilize interestingness measures such as support, confidence, and lift. Interestingness measures are meaningful in that it shows the causes for pruning uninteresting rules statistically or logically. But the criteria of these measures are chosen by experiences, and the number of useful rules is hard to estimate. If too many rules are generated, we cannot effectively extract the useful rules.In this paper, we designed a variety of non-linear regression equations considering all association thresholds between the number of rules and three interestingness measures. And then we diagnosed multi-collinearity and autocorrelation problems, and used analysis of variance results and adjusted coefficients of determination for the best model through numerical experiments.

• Journal of the Korean Society of Hazard Mitigation
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• v.9 no.5
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• pp.125-130
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• 2009

Storage function method which considers the non-linearity of the relationship between rainfall and runoff has been frequently used to predict runoff in a basin and a flood pattern. However, it is time-consuming to estimate appropriate parameters of every basin and rainfall event, which requires the empirical parameter equation applicable in Korea. In this study, multiple regression analysis is used to develop empirical equations to estimate parameters of Storage Function method using basin characteristics. The basin area, maximum stream length, and stream slope are considered as the basin characteristics as the result of the regression analysis. Collinearity is removed and trial-and-error method is used to choose the most descriptive parameters to the dependent variables in Han River basin which is divided into 30 subbasins. The developed equations are validated using the rainfall events in MunMak gauging station and named as 'Han River equation'. The equation could provide the useful information about Storage Function method parameter to calculate runoff from a basin and predict river stage.

• Journal of Korean Society for Geospatial Information Science
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• v.13 no.4 s.34
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• pp.67-73
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• 2005

The urbanization of Korea has been rapidly progressed since 1960, but satellite imagery have provided the information only after 1975. Recently released CORONA imagery is one of the few source of satellite image which can provide 1960's topographic information of the Korean Peninsular. It can be applied to change detection in various fields such as urban, forest, and environmental planning. In this research mosaic image of past Korean Peninsular using CORONA imagery in the 1960s were generated. A polynomial equation and a modified collinearity equation were applied for geo-referencing and a comparative analysis was conducted. In this research the 2nd polynomial equations were used for geo-referencing of CORONA imagery. After carrying out geo-referencing, mosaic image was generated using Erdas Imagine. It is assumed that this result image is very useful for various fields such as generation of thematic maps, urban planning, and change detection.