• Korean Journal of Remote Sensing
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• v.23 no.3
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• pp.189-198
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• 2007

Introduction of lidar technology have contributed to a wide range of applications in generating quality surface models. Accordingly, because of the importance of terrain surface models in mapping applications, rigorous studies have been performed to extract ground points from a lidar data point cloud. Although most filters have been shown abilities to extract ground points with their parameters tuned, however, most experiments revealed that there are certain limitations in optimizing filter parameters and the correction of remaining misclassified points is not straightforward. In this study, therefore, a method to improve the quality of filtered lidar data is proposed, which exploits neighboring surface properties arising between immediate neighbors. The method comprises a sequence of procedures which can reduce commission and omission errors. Commission errors occurring in low-rise objects are reduced by utilizing morphological operations. On the other hand, omission errors are reduced by adding missing ground points around step edges. Experimental results show that the qualities of filtered data can be improved considerably by the proposed method.

• Journal of Korean Society for Atmospheric Environment
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• v.27 no.3
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• pp.326-336
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• 2011

The Light Detection and Ranging (Lidar) observation provides a specific knowledge of the temporal and vertical distribution and the optical properties of the aerosols. Unlike typical Mie scattering Lidars, which can measure backscattering and depolarization, the Raman Lidar can measure the quartz signal at the ultra violet (360 nm) and the visible (546 nm) wavelengths. In this work, we developed a method for estimating mineral quartz concentration immersed in Asian dust using Raman scattering of quartz (silicon dioxide, silica). During the Asian dust period of March 15, 16, and 21 in 2010, Raman lidar measurements detected the presence of quartz, and successfully showed the vertical profile of the dust concentrations. The satellite observations such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) confirmed spatial distribution of Asian dust. This approach will be useful for characterizing the quartz dominated in the atmospheric aerosols and the investigations of mineral dust. It will be especially applicable for distinguishing the dust and non-dust aerosols in studies on the mixing state of Asian aerosols. Additionally, the presented method combined with satellite observations is enable qualitative and quantitative monitoring for Asian dust.

• Proceedings of the KSRS Conference
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• v.2
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• pp.684-687
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• 2006

Airborne imagery must be precisely orthorectified to be used as geographical information data. GPS/INS (Global Positioning System/Inertial Navigation System) and LIDAR (LIght Detection And Ranging) data were employed to automatically orthorectify airborne images. In this study, 154 frame airborne images and LIDAR vector data were acquired. LIDAR vector data were converted to raster image for employing as reference data. To derive images with constant brightness, flat field correction was applied to the whole images. The airborne images were geometrically corrected by calculating internal orientation and external orientation using GPS/INS data and then orthorectified using LIDAR digital elevation model image. The precision of orthorectified images was validated using 50 ground control points collected in arbitrary selected five images and LIDAR intensity image. In validation results, RMSE (Root Mean Square Error) was 0.365 smaller then two times of pixel spatial resolution at the surface. It is possible that the derived mosaicked airborne image by this automatic orthorectification method is employed as geographical information data.

• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.3
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• pp.179-188
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• 2015

We propose obstacle classification method based on 2D LIDAR(Light Detecting and Ranging) database. The existing obstacle classification method based on 2D LIDAR, has an advantage in terms of accuracy and shorter calculation time. However, it was difficult to classifier the type of obstacle and therefore accurate path planning was not possible. In order to overcome this problem, a method of classifying obstacle type based on width data of obstacle was proposed. However, width data was not sufficient to improve accuracy. In this paper, database was established by width, intensity, variance of range, variance of intensity data. The first classification was processed by the width data, and the second classification was processed by the intensity data, and the third classification was processed by the variance of range, intensity data. The classification was processed by comparing to database, and the result of obstacle classification was determined by finding the one with highest similarity values. An experiment using an actual autonomous vehicle under real environment shows that calculation time declined in comparison to 3D LIDAR and it was possible to classify obstacle using single 2D LIDAR.

• Journal of Positioning, Navigation, and Timing
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• v.6 no.1
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• pp.27-33
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• 2017

GPS provides the positioning solution in most areas of the world. However, the position error largely occurs in the urban area due to signal attenuation, signal blockage, and multipath. Although many studies have been carried out to solve this problem, a definite solution has not yet been proposed. Therefore, research is being conducted to solve the vehicle localization problem in the urban environment by converging sensors such as cameras and Light Detection and Ranging (LIDAR). In this paper, the precise vehicle localization using 3D LIDAR (Velodyne HDL-32E) is performed in the urban area. As there are many tall buildings in the urban area and the outer walls of urban buildings consist of planes generally perpendicular to the earth's surface, the outer wall of the building meets at a vertical corner and this vertical corner can be accurately extracted using 3D LIDAR. In this paper, we describe the vertical corner extraction method using 3D LIDAR and perform the precise localization by combining the extracted corner position and GPS/DR information. The driving test was carried out in an about 4.5 km-long section near Teheran-ro, Gangnam. The lateral and longitudinal RMS position errors were 0.146 m and 0.286 m, respectively and showed very accurate localization performance.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.1
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• pp.39-44
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• 2013

Trees are important ground objects that cause oxygen and reduce carbon dioxide in urban areas. For management of the trees, many studies using LIDAR data have been conducted. But, they rely on overseas developed LIDAR data processing software applications because there is a lack of domestically developed software applications. Therefore, this work was intended to propose an automation process that helps to extract trees automatically from LIDAR data. The proposed process has the function to classify LIDAR data and to extract building regions and trees automatically. It was applied to a study place in Yongin to conduct a test. As a result, about 88% of trees were extracted from the automation process.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.29 no.1
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• pp.12-19
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• 2017

There is a need to substitute offshore met-mast with remote sensing equipment such as wind lidar since the initial installation and O&M costs for offshore met-mast are quite high. In this study, applicability of wind lidar is verified by intercomparison test of wind speed and direction data from offshore met-mast and wind lidar for simultaneous operational period. Results at various heights show no statistical difference in trend and size and data from wind lidar is found to be more accurate and have less error than data from offshore met-mast where error from structural shading effect is significant.

• 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.

• 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.575-583
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• 2007

Surface segmentation aims to represent the terrain as a set of bounded and analytically defined surface patches. Many previous segmentation methods have been developed to extract planar patches from LIDAR data for building extraction. However, most of them were not fully satisfactory for more general applications in terms of the degree of automation and the quality of the segmentation results. This is mainly caused from the limited information derived from LIDAR data. The purpose of this study is thus to develop an automatic method to perform surface segmentation by combining not only LIDAR data but also images. A region-based method is proposed to generate a set of planar patches by grouping LIDAR points. The grouping criteria are based on both the coordinates of the points and the corresponding intensity values computed from the images. This method has been applied to urban data and the segmentation results are compared with the reference data acquired by manual segmentation. 76% of the test area is correctly segmented. Under-segmentation is rarely founded but over-segmentation still exists. If the over-segmentation is mitigated by merging adjacent patches with similar properties as a post-process, the proposed segmentation method can be effectively utilized for a reliable intermediate process toward automatic extraction of 3D model of the real world.

• Journal of the Korean Solar Energy Society
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• v.31 no.2
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• pp.63-71
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• 2011

The remote-sensng campaign was performed at the Pohang Accelerator Laboratory where is located in a basin 6km inland from Yeongil Bay. The campaign aimed uncertainty assessment of Remtech PA0 SODAR through a mutual comparison with WindCube LIDAR, the remote-sensing equipment for wind resource assessment. The joint observation was carried out by changing the setup for measurement heights three times over two months. The LIDAR measurement was assumed as the reference and the uncertainty of SODAR measurement was quantitatively assessed. Compared with LIDAR, the data availability of SODAR was about half. The wind speed measurement was fitted to a slope of 0.94 and $R^2$ of 0.79 to the LIDAR measurement. However, the relative standard deviation was about 17% under 150m above ground level. Therefore, the Remtech PA0 SODAR is judged to be unsuitable for the evaluation of wind resource assessment and wind turbine performance test, which require accuracy of measurement.