• Journal of Korean Society for Geospatial Information Science
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• 제19권1호
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• pp.29-36
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• 2011

The main purpose of this paper is to make a map showing canopy cover by using airborne Lidar data based on region. Watershed algorithm was applied to elevation data to conduct segmentation, and then canopy cover was estimated through the regions extracted. In the process of transforming point data to raster, we solved the problems about overestimation and underestimation by using frequency method. Also, canopy cover map could be produced with various scales by differing level of segmentation and it provides more accurate and precise information than ones of ordinary public forest map.

• Journal of the Institute of Electronics Engineers of Korea SP
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• 제46권3호
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• pp.143-151
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• 2009

In this paper, a new extraction algorithm for artificial structure in 3D cloud points of terrestrial LIDAR is described, considering that various obstacles in terrestrial LIDAR make it difficult to apply conventional algorithms which are designed for air-born LIDAR data. Firstly we use the R, G, B color information from the terrestrial LIDAR data to discriminate among the massive 3D cloud points. Hough transform is then applied to estimate the straight lines that correspond to the target structure. Finally, the structure is extracted by comparing the distance between the estimated line and 3D cloud points. The proposed algorithm is efficient in the sense that it requires the user interaction only when the reference colors are obtained. Computer simulation shows the performance to be quite satisfactory.

• Proceedings of the KSRS Conference
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume I
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• pp.500-503
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• 2006

To adjust the discrepancy between Light Detection and Ranging (LIDAR) strips, previous researches generally have been conducted using conjugate features, which are called feature-based approaches. However, irrespective of the type of features used, the adjustment process relies upon the existence of suitable conjugate features within the overlapping area and the ability of employed methods to detect and extract the features. These limitations make the process complex and sometimes limit the applicability of developed methodologies because of a lack of suitable features in overlapping areas. To address these drawbacks, this paper presents a methodology using area-based algorithms. This approach is based on the scheme that discrepancies make complex the local height variations of LIDAR data whithin overlapping area. This scheme can be helpful to determine an appropriate transformation for adjustment in the way that minimizes the geographical complexity. During the process, the contour tree (CT) was used to represent the geological characteristics of LIDAR points in overlapping area and the Iterative Closest Points (ICP) algorithm was applied to automatically determine parameters of transformation. After transformation, discrepancies were measured again and the results were evaluated statistically. This research provides a robust methodology without restrictions involved in methods that employ conjugate features. Our method also makes the overall adjustment process generally applicable and automated.

• Journal of Positioning, Navigation, and Timing
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• 제6권4호
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• pp.159-166
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• 2017

The position of autonomous driving vehicle is basically acquired through the global positioning system (GPS). However, GPS signals cannot be received in tunnels. Due to this limitation, localization of autonomous driving vehicles can be made through sensors mounted on them. In particular, a 3D Light Detection and Ranging (LIDAR) system is used for longitudinal position error correction. Few feature points and structures that can be used for localization of vehicles are available in tunnels. Since lanes in the road are normally marked by solid line, it cannot be used to recognize a longitudinal position. In addition, only a small number of structures that are separated from the tunnel walls such as sign boards or jet fans are available. Thus, it is necessary to extract usable information from tunnels to recognize a longitudinal position. In this paper, fire hydrants and evacuation guide lights attached at both sides of tunnel walls were used to recognize a longitudinal position. These structures have highly distinctive reflectivity from the surrounding walls, which can be distinguished using LIDAR reflectivity data. Furthermore, reflectivity information of tunnel walls was fused with the road surface reflectivity map to generate a synthetic reflectivity map. When the synthetic reflectivity map was used, localization of vehicles was able through correlation matching with the local maps generated from the current LIDAR data. The experiments were conducted at an expressway including Maseong Tunnel (approximately 1.5 km long). The experiment results showed that the root mean square (RMS) position errors in lateral and longitudinal directions were 0.19 m and 0.35 m, respectively, exhibiting precise localization accuracy.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• 제25권5호
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• pp.415-425
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• 2007

In this study, we generated an orthophoto with both LIDAR data and aerial images and compared it with that generated from only the images. For the accuracy assessment of these orthophotos, we performed not only qualitative analysis based on visual inspection but also quantitative analysis by measuring horizontal inconsistency, boundary coordinates and similarity measures on buildings. Based on the visual inspection and horizontal inconsistency, the orthophoto based on LIDAR DSM appeared to be more closer to a true-orthophoto. However, the analysis on measurements of boundary coordinates and similarity measures indicates that the orthophoto based on LIDAR DSM is more vulnerable to double mapping on occluded areas. Accordingly, if we apply an effective solution on double mapping or use only the central areas of the aerial images where occluded areas are rarely founded, we can generate automatically true-orthophotos based on a LIDAR DSM.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• 제24권1호
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• pp.111-121
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• 2006

Recently, many studies have been performed to apply airborne LIDAR data to extracting urban models. In order to model efficiently the man-made objects which are the main components of these urban models, it is important to extract automatically planar patches from the set of the measured three-dimensional points. Although some research has been carried out for their automatic extraction, no method published yet is sufficiently satisfied in terms of the accuracy and completeness of the segmentation results and their computational efficiency. This study thus aimed to developing an efficient approach to automatic segmentation of planar patches from the three-dimensional points acquired by an airborne LIDAR system. The proposed method consists of establishing adjacency between three-dimensional points, grouping small number of points into seed patches, and growing the seed patches into surface patches. The core features of this method are to improve the segmentation results by employing the variable threshold value repeatedly updated through a statistical analysis during the patch growing process, and to achieve high computational efficiency using priority heaps and sequential least squares adjustment. The proposed method was applied to real LIDAR data to evaluate the performance. Using the proposed method, LIDAR data composed of huge number of three dimensional points can be converted into a set of surface patches which are more explicit and robust descriptions. This intermediate converting process can be effectively used to solve object recognition problems such as building extraction.

• 전자공학회논문지 IE
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• 제46권1호
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• pp.7-15
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• 2009

In this paper, automatic structure boundary extraction is proposed using terrestrial LIDAR (Light Detection And Ranging) in 3-dimensional data. This paper describes an algorithm which does not use pictures and pre-processing. In this algorithm, an efficient decimation method is proposed, considering the size of object, the amount of LIDAR data, etc. From these decimated data, object points and non-object points are distinguished using distance information which is a major features of LIDAR. After that, large and small values are extracted using local variations, which can be candidate for boundary. Finally, a boundary line is drawn based on the boundary point candidates. In this way, the approximate boundary of the object is extracted.

• Journal of the Korea Academia-Industrial cooperation Society
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• 제15권5호
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• pp.3213-3219
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• 2014

Currently, increase of greenhouse gas has had a signigicant impact on climate change in urbanization. As a result, the government has been looking for ways to take advantage of the trees that generate oxygen and reduce carbon dioxide for the prevention of climate change. It is essential to extract individual tree for calculating the amount of carbon dioxide reduction of trees. Aerial LIDAR data have three-dimensional information of building as well as trees as form of point clouds. In this study, automated model was developed to extract individual tree using aerial LIDAR data. For this purpose, we established a methodology for extracting trees and then proceeded the process of developing it as an automated model based on model builder of ArcGIS Software. In order to evaluate the applicability of the developed model, the model was compared with commercial software in study area located in Yongin City. Through the experimental result, the proposed model was extract trees 9.91% higher than commercial software. From this results, it was found that the model effectively extracted trees.

• Korean Journal of Optics and Photonics
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• 제27권4호
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• pp.133-142
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• 2016

This paper presents the design and simulation of a three-dimensional pixel-by-pixel scanning light detection and ranging (LIDAR) system with a microelectromechanical system (MEMS) scanning mirror and direct sequence optical code division multiple access (DS-OCDMA) techniques. It measures a frame with $848{\times}480$ pixels at a refresh rate of 60 fps. The emitted laser pulse waves of each pixel are coded with DS-OCDMA techniques. The coded laser pulse waves include the pixel's position in the frame, and a checksum. The LIDAR emits the coded laser pulse waves periodically, without idle listening time to receive returning light at the receiver. The MEMS scanning mirror is used to deflect and steer the coded laser pulse waves to a specific target point. When all the pixels in a frame have been processed, the travel time is used by the pixel-by-pixel scanning LIDAR to generate point cloud data as the measured result.

• Korean Journal of Remote Sensing
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• 제26권1호
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• pp.9-20
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• 2010

The LIDAR data can be efficiently utilized for automatic reconstruction of 3D models of objects on the terrain and the terrain itself. In this paper, we attempted to generate simulated waveforms of FW (Full-Waveform) LIDAR (LIght Detection And Ranging). We performed the geometric modeling of the sensor and objects, and the radiometric modeling of the waveform intensity. First, we compute the origins and directions of the sub-beams by considering the divergence effects of a laser beam. We then searched for the locations at which the sub-beams intersected with the objects, such as ground, buildings and trees. Finally, we generate the individual waveforms of the reflected sub-beams and the waveform of the entire beam by summing the individual ones. With the experimental results, we confirmed the waveforms were reasonably generated, showing the characteristics of the surfaces the beam interacted with.