• 한국공간정보시스템학회:학술대회논문집
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• 2005.05a
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• pp.287-291
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• 2005

LiDAR 자료로부터 3차원 공간 정보체계를 구축하기 위하여 LiDAR 포인트를 지면과 비지면 포인트로 분리하고 비지면 포인트를 각각의 개체(건물, 수목, 기타 인공 구조물 등)별로 분리하는 과정이 필수적이다. 그러나 LiDAR 자료는 불규칙한 분포의, 방대한 양의 포인트로 구성되어 있어 이를 처리하기 위한 특별한 형태의 자료 구조 체계의 구축이 필요하다. 본 연구에서는 유사한 고도를 갖는 인접한 포인트들로 클래스를 형성하여, 새로운 포인트에 대하여 기존 클래스 포인트들과의 인접성 및 고도 유사성을 검토함으로써 분류를 수행하였다. 이를 위하여 원 LiDAR 자료 구조를 이용하였으며 결과적으로 지면과 비지면의 분리 및 각 비지면 개체간의 분리를 동시에 수행할 수 있었다.

• Korean Journal of Remote Sensing
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• v.22 no.6
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• pp.533-542
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• 2006

Lidar point clouds provide three dimensional information of terrain surface and have a great advantage to generate precise digital elevation model (DEM), particularly over forested area where some laser signals are transmitted to vegetation canopy and reflected from the bare ground. This study initially investigates the characteristics of lidar-derived height information as related to vertical structure of forest stands. Then, we propose a new filtering method to separate ground points from Lidar point clouds, which is a prerequisite process both to generate DEM surface and to extract biophysical information of forest stands. Laser points clouds over the forest stands in central Korea show that the vertical distribution of laser points greatly varies by the stand characteristics. Based on the characteristics, the proposed filtering method processes first and last returns simultaneously without setting any threshold value. The ground points separated by the proposed method are used to generate digital elevation model, furthermore, the result provides the possibilities to extract other biophysical characteristics of forest.

• Journal of Advanced Navigation Technology
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• v.27 no.4
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• pp.481-487
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• 2023

Recently, interest in UAM (Urban Air Mobility) has surged as a critical solution to urban traffic congestion and air pollution issues. However, efficient UAM operation requires accurate 3D Point Cloud data processing, particularly in separating the ground and objects. This paper proposes and validates a method for effectively separating ground and objects in a UAM environment, taking into account its dynamic and complex characteristics. Our approach combines attitude information from MEMS sensors with ground plane estimation using RANSAC, allowing for ground/object separation that isless affected by GPS errors. Simulation results demonstrate that this method effectively operates in UAM settings, marking a significant step toward enhancing safety and efficiency in urban air mobility. Future research will focus on improving the accuracy of this algorithm, evaluating its performance in various UAM scenarios, and proceeding with actual drone tests.

• Proceedings of the KSRS Conference
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• 2007.03a
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• pp.142-147
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• 2007

본 연구에서는 스캔라인을 이용한 LIDAR 포인트 cloud의 분리과정 중 분리된 포인트 군집간 인접 관계를 인식할 수 있는 기능을 추가하였다. 군집간 인접관계는，포인트 cloud 분리 과정 중에 분리된 건물 요소를 재결합하거나 지면 포인트를 인식하기 위하여 사용될 수 있다. 실험 결과 포인트 cloud 분리 과정에 군집간 인접 관계 인식 기능을 추가하더라도 처리 성능이 저하되지 않았으며 후처리를 통하여 건물 요소를 결합하여 온전한 형태의 건물 포인트 군집을 형성함과 더불어 지면 포인트 군집도 인식할 수 있음을 확인하였다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.4
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• pp.359-365
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• 2011

This study aims at filtering ALS points into ground and non-ground effectively through labeling and window based algorithm by utilizing 2D adjacency based on scan line. Firstly, points adjacency is constructed through minimal search based on scan line. Connected component labeling algorithm is applied to classify raw ALS points into ground and non-ground by utilizing the adjacency structure. Then, some small objects are removed by morphology filtering, and isolated ground points are restored by IDW estimation. The experimental results shows that the method provides good filtering performance( about 97% accuracy) for diverse sites, and the overall processing takes less time than converting raw data into TIN or raster grid.

• Korean Journal of Remote Sensing
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• v.39 no.5_2
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• pp.827-835
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• 2023

Drone light detection and ranging (LiDAR) is a state-of-the-art surveying technology that enables close investigation of the top of the mountain slope or the inaccessible slope, and is being used for field surveys in mountainous terrain. To build topographic information using Drone LiDAR, a preprocessing process is required to effectively separate ground and non-ground points from the acquired point cloud. Therefore, in this study, the point group data of the mountain topography was acquired using an aerial LiDAR mounted on a commercial drone, and the application and accuracy of the cloth simulation filtering algorithm, one of the ground separation techniques, was verified. As a result of applying the algorithm, the separation accuracy of the ground and the non-ground was 84.3%, and the kappa coefficient was 0.71, and drone LiDAR data could be effectively used for landslide field surveys in mountainous terrain.

• Journal of Korean Society for Geospatial Information Science
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• v.15 no.1 s.39
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• pp.17-22
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• 2007

In this study, we recognized and joined parts of buildings separated during LIDAR point segmentation utilizing scan line characteristics, with an additional function to recognize neighboring relation among point segments. And we applied the relation to suggest a method to recognize earth point segment. From the test, we could confirm that it does not drop down the efficiency of point segmentation to be added with the function of recognizing neighboring relation and it is possibile to combine point segments to form a complete shaped building and to recognize earth point segment.

• 한국공간정보시스템학회:학술대회논문집
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• 2005.05a
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• pp.293-298
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• 2005

LIDAR의 표고점 데이터는 건물, 수목 등의 개체를 구성하는 비지면점과 순수한 지표면을 나타내는 지면점들이 섞여있기 때문에 이들을 분리하는 과정이 필요하다. 지금까지 연구된 방법들은 몇 가지 입력 요소가 필요하여 완전 자동화를 이루지는 못하고 있으며, 다양한 크기의 개체를 동시에 자동으로 찾아내기 어렵고 경사진 지형에 대해서는 적용하기 어려운 문제점을 가지고 있다. 이에 본 논문에서는 원 데이터의 동일 스캔 라인 상에 존재하는 이웃 점들 간의 경사를 이용하여 입력 요소를 최소화하여 개체를 추출하고자 한다. 이웃하는 두 점플 간의 경사를 이용하여 비지면점을 탐지하여 이웃하는 지면점의 높이 값으로 대체하며 갱신된 값을 바로 다음 연산에 반영시킴으로써 윈도우를 사용하거나 그룹화 할 필요가 없다. 또한 갱신된 값을 전파시키기 때문에 복잡한 지붕을 가지는 건물도 추출할 수가 있다. 이와 같은 연산을 두 방향에 대하여 수행하여 경사진 지형에 대하여 적용할 수 있도록 하였으며 천안과 마산지역에 대하여 테스트를 수행하였다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.1
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• pp.1-9
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• 2020

ABL (Airborne Bathymetric LiDAR) is an advanced survey technology that uses green lasers to simultaneously measure the water depths and oceanic topography in coastal and river areas. Seabed point cloud extraction is an essential prerequisite to further utilizing the ABL data for various geographic data processing and applications. Conventional seabed detection approaches often use return waveforms. However, their limited accessibility often limits the broad use of the bathymetric LiDAR (Light Detection And Ranging) data. Further, it is often questioned if the waveform-based seabed extraction is reliable enough to extract seabed. Therefore, there is a high demand to extract seabed from the point cloud using other sources of information, such as geometric information. This study aimed to assess the feasibility of a ground filtering method to seabed extraction from geo-referenced point cloud data by using CSF (Cloth Simulation Filtering) method. We conducted a preliminary experiment with the RIGEL VQ 880 bathymetric data, and the results show that the CSF algorithm can be effectively applied to the seabed point segmentation.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.4
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• pp.171-181
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• 2013

Recently, ground penetrating radar(GPR) has been widely used in detecting metallic and nonmetallic buried landmines and a number of related researches have been reported. A novel preprocessing method is proposed in this paper to flag potential locations of buried mine-like objects from GPR array measurements. GPR operates by measuring the reflection of an electromagnetic pulse from discontinuities in subsurface dielectric properties. As the GPR pulse propagates in the geologic medium, it suffers nonlinear attenuation as the result of absorption and dispersion, besides spherical divergence. In the proposed algorithm, a logarithmic transformed regression model which successfully represents the time-varying signal amplitude of the GPR data is estimated at first. Then, background signals may be densely distributed near the regression model and candidate signals of targets may be far away from the regression model in the time-amplitude space. Based on the observation, GPR signals are decomposed into candidate signals of targets and background signals using residuals computed from the estimated value by regression and the measurement of GPR. Candidate signals which may contain target signals and noise signals need to be refined. Finally, targets are detected through the refinement of candidate signals based on geometric signatures of mine-like objects. Our algorithm is evaluated using real GPR data obtained from indoor controlled environment and the experimental results demonstrate remarkable performance of our mine-like object detection method.