• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2007.04a
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• pp.201-205
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• 2007

In the post-Cold War era, acquisition technique of high-resolution satellite imagery (HRSI) has begun to commercialize. IKONOS-2 satellite imaging data is supplied for the first time in the 21st century. Many researchers testified mapping possibility of the HRSI data instead of aerial photography. It is easy to renew and automate a topographical map because HRSI not only can be more taken widely and periodically than aerial photography, but also can be directly supplied as digital image. In this study matching size of IKONOS Geo-level stereo image is presented lot production of digital elevation model (DEM). We applied area based matching method using correlation coefficient of pixel brightness value between the two images. After matching line (where "matching line" implies straight line that is approximated to complex non-linear epipolar geometry) is established by exterior orientation parameters (EOPs) to minimize search area, the matching is tarried out based on this line. The experiment on matching size is performed according to land cover property, which is divided off into four areas (water, urban land, forest land and agricultural land). In each of the test areas, window size for the highest correlation coefficient is selected as propel size for matching. As the results of experiment, the proper size was selected as $123{\times}123$ pixels window, $13{\times}13$ pixels window, $129{\times}129$ pixels window and $81{\times}81$ pixels window in the water area, urban land, forest land and agricultural land, respectively. Of course, determination of the matching size by the correlation coefficient may be not absolute appraisal method. Optimum matching size using the geometric accuracy therefore, will be presented by the further work.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.6_2
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• pp.587-597
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• 2007

This study determines matching size for digital elevation model (DEM) production according to land cover property from IKONOS Geo-level stereo image. We applied area based matching method using correlation coefficient of pixel brightness value between the two images. After matching line (where "matching line" implies straight line that is approximated to complex non-linear epipolar geometry) is established by exterior orientation parameters to minimize search area, the matching is carried out based on this line. The experiment is performed according to land cover property, which is divided off into four areas (water, urban land, forest land and agricultural land). In each of the test areas, matching size is selected using a correlation-coefficient image and parallax image. As the results, optimum matching size of the images was selected as $81{\times}81$ pixels window, $21{\times}21$ pixels window, $119{\times}119$ pixels window and $51{\times}51$ pixels window in the water area, urban land, forest land and agricultural land, respectively.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.14 no.1
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• pp.9-16
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• 1996

A new stereo matching method for the relative orientation and the photogrammetric triangulation has been pro-posed. It matches the pairs of conjugate points to be used as pass points and tie points in digital aerial photographs instead of selecting these points with manual point transfer and measurements. Three unique steps included in the proposed matching method are as followings. The first step is searching interest points, and designating them as candidate points to be matched. The second is matching the points from the pair of images by the Cross Correlation Method in both direction(left to right direction and right to left). The third is selecting consistent pairs in the both matching directions. Computer programs based on the proposed matching method have been developed, and with digital aerial photographs which have full ground coordinate information tests were performed to know reliabiliy and positional accuracy of proposed method. Results of the tests reveal that the proposed matching method can eliminate the in-correctly matched pairs more efficiently than other matching methods, thus this can be more reliably applied to the relative orientation and the photogrammetric triangulation.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.1
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• pp.39-47
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• 2014

In this study, the epipolar images were created by both methods of resolution adjustment and piecewise approach using RPC(Rational Polynomial coefficients) and ancillary data of IKONOS-2 and SPOT-5 satellite images whose resolutions are different from each other. The stereo geometry of these two satellite images was analyzed and the RPC block modelling was accomplished for generating epipolar images. In order to evaluate the accuracy of created epipolar images, the y-parallaxes were analyzed for the specific points which were apparently identified in mountainous, plain and urban area. Also the RMSEs of the specific points were calculated using the coordinates from the epipolar stereo images and the coordinates from the block triangulation. Y-parallaxes were within one pixel and the RMSEs were within two meters for X, Y and Z each.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.9 no.2
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• pp.93-102
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• 1991

This study aims to develop techniques for generating Digital Elevation Model(DEM) from SPOT Computer Compatible Tape(CCT) data, so as to present an effective way of generation of DEM for large area. As the first phase of extracting ground heights from SPOT stereo digital data, the bundle adjustment technique was used to determine the satellite exterior orientation parameters. Because SPOT data has the characteristics of multiple perspective projection, exterior orientation Parameters were modelled as a function of scan lines. In the second phase, a normalized cross correlation matching technique was applied to search for the conjugate pixels ill stereo pairs. The preliminary study showed that the matching window size of 13$\times$13 was adequate. After image coordinates of the conjugate pixels were determined by the matching technique, the ground coordinates of the corresponding pixels were calculated by the space intersection method. Then DEM was generated by interpolations. In addtion an algorithm for the elimination of abnormal elevation was developed and applied. The algorithm was very effective to improve the accuracy of the generated DEM.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.4
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• pp.359-365
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• 2008

This paper focused on generation of more accurate DEM and analysis of accuracy. For this, we applied suitable sensor modeling technique for each satellite image and automatic pyramid matching using image pyramid was applied. Matching algorithm based on epipolarity and scene geometry also was applied for stereo matching. IKONOS, Quickbird, SPOT-5, Kompsat-2 were used for experiments. In particular, we applied orbit-attitude sensor modeling technique for Kompsat-2 and performed DEM generation successfully. All DEM generated show good quality. Assessment was carried out using USGS DTED and we also compared between DEM generated in this research and DEM generated from common software. All DEM had $9m{\sim}12m$ Mean Absolute Error and $13m{\sim}16m$ RMS Error. Experimental results show that the DEMs of good performance which is similar to or better than result of DEMs generated from common software.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.3
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• pp.269-279
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• 2020

Traffic estimation mainly involves surveying equipment such as automatic vehicle classification, vehicle detection system, toll collection system, and personnel surveys through CCTV (Closed Circuit TeleVision), but this requires a lot of manpower and cost. In this study, we proposed a method of estimating traffic volume using deep learning and stereo CCTV to overcome the limitation of not detecting the entire vehicle in case of single CCTV. COCO (Common Objects in Context) dataset was used to train deep learning models to detect vehicles, and each vehicle was detected in left and right CCTV images in real time. Then, the vehicle that could not be detected from each image was additionally detected by using affine transformation to improve the accuracy of traffic volume. Experiments were conducted separately for the normal road environment and the case of weather conditions with fog. In the normal road environment, vehicle detection improved by 6.75% and 5.92% in left and right images, respectively, than in a single CCTV image. In addition, in the foggy road environment, vehicle detection was improved by 10.79% and 12.88% in the left and right images, respectively.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.3
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• pp.167-174
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• 2017

Antarctica, where ice sheet has been declined rapidly, should be monitored periodically. However, there are difficult to access for local survey or aircraft observation due to the vast and extreme environments of the polar regions. In order to overcome this problem, there have been a lot of studies by acquiring radar or laser data by satellite. It is also difficult to accurately measure the changes of the surface where is composed of snow or ice layer, and it is also difficult to product a high-resolution DEM. This study therefore aims to product DEMs of two periods using high-resolution KOMPSAT-3A stereo images, and DEM matching is implemented by the LZD(Least-squares Z-Differences) method to detect DEM changes in both periods. As a result, the proposed method could be suggested as comparing height differences of the two DEMs within 1m precision.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.3
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• pp.101-108
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• 2019

Due to recent improvements in computer processing speed and image processing technology, researches are being actively carried out to combine information from camera with existing GNSS (Global Navigation Satellite System) and dead reckoning. In this study, developed a vision-based positioning assistant algorithm to estimate the distance to the object from stereo images. In addition, GNSS/on-board vehicle sensor/vision based positioning algorithm is developed by combining vision based positioning algorithm with existing positioning algorithm. For the performance analysis, the velocity calculated from the actual driving test was used for the navigation solution correction, simulation tests were performed to analyse the effects of velocity precision. As a result of analysis, it is confirmed that about 4% of position accuracy is improved when vision information is added compared to existing GNSS/on-board based positioning algorithm.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.5
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• pp.407-414
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• 2015

Interest points are generally located at the pixels where height changes occur. So, interest points can be the significant pixels for DSM generation, and these have the important role to generate accurate and reliable matching results. Manual operation is widely used to extract the interest points and to match stereo satellite images using these for generating height information, but it causes economic and time consuming problems. Thus, a tie point extraction method using Harris-affine technique and SIFT(Scale Invariant Feature Transform) descriptors was suggested to analyze height information of buildings in this study. Interest points on buildings were extracted by Harris-affine technique, and tie points were collected efficiently by SIFT descriptors, which is invariant for scale. Searching window for each interest points was used, and direction of tie points pairs were considered for more efficient tie point extraction method. Tie point pairs estimated by proposed method was used to analyze height information of buildings. The result had RMSE values less than 2m comparing to the height information estimated by manual method.