• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.3
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• pp.191-198
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• 2007

According to the survey data during the Japanese Occupation Period, the length of South Korea's coastline is about 11,542km, including the coastlines of mainland and islands. To accurately revise/renew this coastline data through site survey, it will cost great money and time. Also, various development projects such as reclamation works on public waters, constructions of ports/harbors, etc. This paper used aerial photographs, satellite image data and GPS survey data with certain intervals to monitor the change in coastal areas of Songieong, Haeundae, Kwanganri, Songdo and Dadaepo. The local area subjected for this research was limited to areas near Busan. The specific contents of this research include. Launching qualitative/time series analyses on the change of coastal areas using aerial photographs, satellite image data and RTK-GPS surveys.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.553-558
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• 2002

Physical sensor model in pushbroom satellite images can be made from sensor modeling by rotation parameters and attitude parameters on the satellite track. These parameters are determined by the information obtained from GPS, INS, or star tracker. Provided from satellite image, an auxiliary data error is connected directly with an error of rotation parameters and attitude parameters. This paper analyzed how obtaining satellite images influenced errors of rotation parameters and attitude parameters. furthermore, for detailed analysis, this paper generated simulated satellite image, which was changed variously by rotation parameters and attitude parameters of satellite sensor model. Simulated satellite image is generated by using high-resolution digital aerial image and DEM (Digital Elevation Model) data. Moreover, this paper determined correlation of rotation parameter and attitude parameters through error analysis of simulated satellite image that was generated by various rotation parameters and attitude parameters.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.248-250
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• 2003

An automatic approach and strategy for extracting building information from aerial images using combined image analysis and interpretation techniques is described in this paper. A dense DSM is obtained by stereo image matching. Multi-band classification, DSM, texture segmentation and Normalised Difference Vegetation Index (NDVI) are used to reveal building interest areas. Then, based on the derived approximate building areas, a shape modelling algorithm based on the level set formulation of curve and surface motion has been used to precisely delineate the building boundaries. Data fusion, based on the Dempster-Shafer technique, is used to interpret simultaneously knowledge from several data sources of the same region, to find the intersection of propositions on extracted information derived from several datasets, together with their associated probabilities. A number of test areas, which include buildings with different sizes, shape and roof colour have been investigated. The tests are encouraging and demonstrate that the system is effective for building extraction, and the determination of more accurate elevations of the terrain surface.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.6
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• pp.669-678
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• 2009

Real-time image georeferencing technologies are required to generate spatial information promptly from the image sequences acquired by a multi-sensor system. We thus derive a sequential adjustment algorithm based on the combined adjustment model. By adopting the sequential adjustment model, we develop a sequential AT(Aerial Triangulation) algorithm to georeference image sequences in real-time. The proposed algorithm enables to perform AT rapidly with the minimum computation at the current stage by using the results computed at the previous stage whenever a new image is added. Experiments with simulated data were conducted to verify the effectiveness of the proposed algorithm. The results of the experiments show that the georeferencing of each image took very short time and its accuracy was determined within ${\pm}4cm$ on the ground control points comparing to the results of the existing simultaneous AT.

• 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.

• Korean Journal of Remote Sensing
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• v.23 no.5
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• pp.409-420
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• 2007

Since 1988, pine wilt disease has spread over rapidly in Korea. It is not easy to detect the damaged pine trees by pine wilt disease from conventional remote sensing skills. Thus, many possibilities were investigated to detect the damaged pines using various kinds of remote sensing data including high spatial resolution satellite image of 2000/2003 IKONOS and 2005 QuickBird, aerial photos, and digital airborne data, too. Time series of B&W aerial photos at the scale of 1:6,000 were used to validate the results. A local maximum filtering was adapted to determine whether the damaged pines could be detected or not at the tree level from high resolution satellite images, and to locate the damaged trees. Several enhancement methods such as NDVI and image transformations were examined to find out the optimal detection method. Considering the mean crown radius of pine trees, local maximum filter with 3 pixels in radius was adapted to detect the damaged trees on IKONOS image. CIR images of 50 cm resolution were taken by PKNU-3(REDLAKE MS4000) sensor. The simulated CIR images with resolutions of 1 m, 2 m, and 4 m were generated to test the possibility of tree detection both in a stereo and a single mode. In conclusion, in order to detect the pine tree damaged by pine wilt disease at a tree level from satellite image, a spatial resolution might be less than 1 m in a single mode and/or 1 m in a stereo mode.

• Journal of Korean Society for Geospatial Information Science
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• v.2 no.2 s.4
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• pp.131-142
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• 1994

Generally, there are two methods which generates the base map of Geo-Spatial Information System(GSIS). one is the digitizing of existing map, and the other is the analytical plotting method editing data acquired by sensors using computers. But the analytical plotting method and method of the digitizing of existing map is technically complex and has the disadvantages in the costs and time. The subject region of study(the Kwangyang province), was photographed by aircraft, and photographing scale was 1/6,000. Then this area was divided into two specific regions, the residential area, and the agricultural area. In this study, we developed the algorithm that generated base map of database in GSIS from the aerial photo. This algorithm is as followed. First, the digital aerial photos were generated using these aerial photos. Second, these digital aerial photos were enhanced by implementing the histogram equalization. Third, the objects of the enhanced images were extracted by implementing thresholding and edged detection techiques of image segmentation. Finally, these images could be used to updated the base map of database in GSIS. The result obtained from this study showed that method used by this study were more efficient than existing method in costs and time.

• Journal of Korean Society for Geospatial Information Science
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• v.11 no.1 s.24
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• pp.51-59
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• 2003

This study intended to extract the spatial data and attribute data from the images of terrestrial and aerial photographs and to compile the digital map from the images using various kinds of photogrammetric analysis. The Three Dimensional Frame Model (3DFM) was produced from multiple images of terrestial photographs, and the Three Dimensional Photo Image Model (3DPIM) was made using 3DFM and image patches of terrestrial photo, which is useful for identifying the feature and characteristics of the object. In addition, the spatial data base for the buildings, roads and supplementary facilities in the objective area was updated by the vectorizing procedures with small scale areal photos.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.6_1
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• pp.529-536
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• 2007

Registration between image and object space is a fundamental step in photogrammetry and computer vision. Along with rapid development of sensors - multi/hyper spectral sensor, laser scanning sensor, radar sensor etc., the needs for registration between different sensors are ever increasing. There are two important considerations on different sensor registration. They are sensor invariant feature extraction and correspondence between them. Since point to point correspondence does not exist in image and laser scanning data, it is necessary to have higher entities for extraction and correspondence. This leads to modify first, existing mathematical and geometrical model which was suitable for point measurement to line measurements, second, matching scheme. In this research, linear feature is selected for sensor invariant features and matching entity. Linear features are incorporated into mathematical equation in the form of extended collinearity equation for registration problem known as photo resection which calculates exterior orientation parameters. The other emphasis is on the scheme of finding matched entities in the aide of RANSAC (RANdom SAmple Consensus) in the absence of correspondences. To relieve computational load which is a common problem in sampling theorem, deterministic sampling technique and selecting 4 line features from 4 sectors are applied.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.4
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• pp.231-240
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• 2017

This paper aims to study a method to estimate precise carbon absorption by quantification of forest information that uses accurate LiDAR data, hyperspectral image. To estimate precise carbon absorption value by using spatial data, a problem was found out of carbon absorption value estimation method with statistical method, which is already existed method, and then offered optimized carbon absorption estimation method with spatial information by analyzing with methods of compare digital aerial photogrammetry and LiDAR data. It turned out possible Precise classification and quantification in case of using LiDAR and hyperspectral image. Various classification of tree species was possible with use of LiDAR and hyperspectral image. Classification of hyperspectral image was matched in general with field survey and Mahalanobis distance classification method. Precise forest resources could be extracted using high density LiDAR data. Compared with existing method, 19.7% in forest area, 19.2% in total carbon absorption, 0.9% in absorption per unit area of difference created, and improvement was found out to be estimated precisely in international code.