• 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 Korean Society for Geospatial Information Science
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• v.18 no.2
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• pp.119-129
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• 2010

Many algorithms for processing LIDAR data have been developed for diverse applications not limited to patch segmentation, bare-earth filtering and building extraction. However, since we cannot exactly know the true locations of individual LIDAR points, it is difficult to assess the performance of a LIDAR data processing algorithm. In this paper, we thus attempted the performance assessment of the segmentation algorithm developed by Lee (2006) using the LIDAR data generated through simulation based on sensor modelling. Consequently, based on simulation, we can perform the performance assessment of a LIDAR processing algorithm more objectively and quantitatively with an automatic procedure.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.3
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• pp.173-179
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• 2015

In this paper, object-based classification of urban areas based on a combination of information from lidar and aerial images is introduced. High resolution images are frequently used in automatic classification, making use of the spectral characteristics of the features under study. However, in urban areas, pixel-based classification can be difficult since building colors differ and the shadows of buildings can obscure building segmentation. Therefore, if the boundaries of buildings can be extracted from lidar, this information could improve the accuracy of urban area classifications. In the data processing stage, lidar data and the aerial image are co-registered into the same coordinate system, and a local maxima filter is used for the building segmentation of lidar data, which are then converted into an image containing only building information. Then, multiresolution segmentation is achieved using a scale parameter, and a color and shape factor; a compactness factor and a layer weight are implemented for the classification using a class hierarchy. Results indicate that lidar can provide useful additional data when combined with high resolution images in the object-oriented hierarchical classification of urban areas.

• Journal of the Korean Society of Hazard Mitigation
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• v.4 no.4 s.15
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• pp.55-61
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• 2004

Rapid developments in sensor technologies now allow the generation of multi-source topographical data. For many applications, however, the geospatial information provided by individual sensors is not complete, precise, and consistent. To solve these inherent problems, additional diverse sources of complementary data can be used and fused. In this paper, the experiment was done for generation of 3D orthoimage data using LIDAR data and digital camera image. And the results show that 3D orthoimage can be used for the flood monitoring.

• 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 the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.24 no.1
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• pp.139-145
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• 2006

The advantage of the lidar data is in fast acquisition and process time as well as in high accuracy and high point density. However lidar data itself is difficult to classify the earth surface because lidar data is in the form of irregularly distributed point clouds. In this study, we investigated land cover classification using both lidar data and optical image through a supervised classification method. Firstly, we generated 1m grid DSM and DEM image and then nDSM was produced by using DSM and DEM. In addition, we had made intensity image using the intensity value of lidar data. As for optical images, the red, blue, green band of CCD image are used. Moreover, a NDVI image using a red band of the CCD image and infrared band of IKONOS image is generated. The experimental results showed that land cover classification with lidar data and optical image together could reach to the accuracy of 74.0%. To improve classification accuracy, we further performed re-classification of shadow area and water body as well as forest and building area. The final classification accuracy was 81.8%.

• Journal of Korean Society of Forest Science
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• v.101 no.1
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• pp.20-27
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• 2012

The flowed soil volume is able to be estimated simply from topographic data of before and after the debris flow. However, it is often difficult to obtain high resolution topographic data before debris flow because debris flow was occurred in mountainous area and airborne Lidar data was mainly surveyed in urban area. For this reason, Woo(2011) developed the topographic restoration method that can reconstruct the topography before the debris flow using airborne Lidar data. In this study, we applied the topographic restoration method on Inje county, Bongwha county and Jecheon city, produced topography data before debris flow that RMSE is from 0.16 to 0.34 m. Also, a soil variation was analyzed by topography data before and after debris flow, and it was used to estimate a real soil volume flowed to downstream and a spatial distribution showing collapses, flows, sedimentations appeared to debris flow.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6D
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• pp.923-929
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• 2008

Due to the increasing need for 3D spatial data, modeling of topography and artificial structures plays an important role in three-dimensional Urban Analysis. This study suggests a methodology for solving the problem of calculation for the extraction of building boundary, minimizing the user's intervention, and automatically extracting building boundary, using the LIDAR data. The methodology suggested in this study is characterized by combining the merits of the point-based process and the image-based process. The procedures for extracting building boundary are three steps: 1) LIDAR point data are interpolated to extract approximately building region. 2) LIDAR point data are triangulated in each individual building area. 3) Extracted boundary of each building is then simplified in consideration of its area, minimum length of building.The performance of the developed methodology is evaluated using real LIDAR data. Through the experiment, the extracted building boundaries are compared with digital map.

• 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.

• 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.