• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.4
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• pp.516-523
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• 2009

Recently, the applications of unmanned robots are increasing in various fields including surveillance and reconnaissance, planet exploration and disaster relief. To perform their missions with success, the robots should be able to evaluate terrain's characteristics quantitatively and identify traversable regions to progress toward a goal using mounted sensors. Recently, the authors have proposed techniques that extract terrain information and analyze traversability for off-road navigation of an unmanned robot. In this paper, we examine the use of 3D world models(terrain maps) to classify obstacle and safe terrain for increasing the reliability of the proposed techniques. A world model is divided into several patches and each patch is classified as belonging either to an obstacle or a non-obstacle using three types of metrics. The effectiveness of the proposed method is verified on real terrain maps.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.708-708
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• 2002

Recent developments in sensor technologies have provided remotely sensed data with very high spatial resolution. In order to fully utilize the potential of high resolution images, new image classification strategies are necessary. Unfortunately, the high resolution images increase the spectral within-field variability, and the classification accuracy of traditional methods based on pixel-based classification algorithms such as Maximum-Likelihood method may be decreased (Schiewe 2001). Recent development in Object Oriented Classification based on image segmentation algorithms can be used for the classification of forest patches on rugged terrain of Korea. The objectives of this paper are as follows. First, to compare the pros and cons of image classification methods based on pixel-based and object oriented classification algorithm for the forest patch classification. Landsat ETM＋ data and IKONOS data will be used for the classification. Second, to investigate ways to increase classification accuracy of forest patches. Supplemental data such as DTM and Forest Type Map of 1:25,000 scale are used for topographic correction and image segmentation. Third, to propose the best classification strategy for forest patch classification in terms of accuracy and data requirement. The research site for this paper is Namhansansung Provincial Park located at the eastern suburb of Seoul Metropolitan City for its diverse forest patch types and data availability. Both Landsat ETM＋ and IKONOS data are used for the classification. Preliminary results can be summarized as follows. First, topographic correction of reflectance is essential for the classification of forest patches on rugged terrain. Second, object oriented classification of IKONOS data enables higher classification accuracy compared to Landsat ETM＋ and pixel-based classification. Third, multi-stage segmentation is very useful to investigate landscape ecological aspect of forest communities of Korea.

• Journal of Forest and Environmental Science
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• v.9 no.1
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• pp.57-66
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• 1993

The purpose of this study is to offer detailed topographic information for substantially selecting the yarding machine for mechanized yarding operations, classifying the forest site by cluster analysis and principal component analysis, and investigating simultaneously the variables which give much influence on the classification of forest site in forestry build-up region (21, 477ha) of Chunchon Gun, Kwangweon Do. Ten topographic variables were used for the analysis. The results of study were as follows : 1) Gosung region (2, 252ha) was classified into hilly terrain (57%) and steep terrain (43%) and required the tractor prehauling system for the former one and the medium skyline system for latter one, respectively. 2) 65% of Gajung region (2,306ha) and 67% of Kwangpan region (2, 627ha) were classified into steep terrain fitted for the medium skyline system and the portion of both region showed the hilly terrain for the tractor prehauling system. 3) Jiam region (4, 591ha), consisted only of steep terrain, required the medium skyline system. 4) Gunja region (3, 400ha), Sudong region (3, 984ha) and Sinpo region (2, 340ha) were classified into steep terrain, requiring the medium skyline system, with 85%, 75%, and 75%, respectively.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.463-467
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• 2006

Several segmentation algorithms have been proposed for DTM generation or building modeling from airborne LiDAR data. Three components are important for accurate segmentation: (i) the adjacent relationship of n-nearest points or mesh, etc. (ii) the effective decision parameters of height, slope, curvature, and plane condition, (iii) grouping methods. In this paper, we created the topology of point cloud data using the contour tree and implemented the region-growing Terrain and non-terrain points were classified correctly in the segmented data, which can be used also for feature classification.

• Korean Journal of Remote Sensing
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• v.25 no.6
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• pp.531-546
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• 2009

The main objective of this study is to analyse the polarimetric characteristics of the various terrain targets by ground-based polarimetric SAR system and to confirm the compatible and effective polarimetric analysis method to reveal the polarization properties of different terrain targets by the GB-SAR. The fully polarimetric GB-SAR data with HH, HV, VH, and VV components were focused using the Deramp-FFT (DF) algorithm. The focused GB-SAR images were processed by the H/A/$\alpha$ polarimetric decomposition and the combined H/$\alpha$ or H/A/$\alpha$ and Wishart classification method. The segmented image and distribution graphs in H/$\alpha$ plane using Cloude and Pottier's method showed a reliable result that this quad-polarization GB-SAR data could be useful to classified corresponding scattering mechanism. The H/$\alpha$-Wishart and H/A/$\alpha$-Wishart classification results showed that a natural media and an artificial target were discriminated by the combined classification, in particular, after applying multi-looking and the Lee refined speckle filter.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.3
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• pp.251-257
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• 2012

Unmanned ground vehicles have important military, reconnaissance, and materials handling application. Many of these applications require the UGVs to move at high speeds through uneven, natural terrain with various compositions and physical parameters. This paper presents a framework for high speed autonomous navigation based on the integrated real time traversability. Specifically, the proposed system performs real-time dynamic simulation and calculate maximum traversing velocity guaranteeing safe motion over rough terrain. The architecture of autonomous navigation is firstly presented for high-speed autonomous navigation. Then, the integrated real time traversability, which is composed of initial velocity profiling step, dynamic analysis step, road classification step and stable velocity profiling step, is introduced. Experimental results are presented that demonstrate the method for a $6{\times}6$ autonomous vehicle moving on flat terrain with bump.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.2
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• pp.29-33
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• 2010

The purpose of LiDAR ground classification is to archive both goals which are acquiring confident ground points with high precision and describing ground shape in detail. In spite of many studies about developing optimized algorithms to kick out this, it is very difficult to classify ground points and describing ground shape by airborne LiDAR data. Especially it is more difficult in a dense forested area like Korea. Principle misclassification was mainly caused by complex forest canopy hierarchy in Korea and relatively coarse LiDAR points density for ground classification. Unfortunately, a lot of LiDAR surveying performed in summer in South Korea. And by that reason, schematic LiDAR points distribution is very different from those of Europe. So, this study propose enhanced ground classification method considering Korean land cover characteristics. Firstly, this study designate highly confident candidated LiDAR points as a first ground points which is acquired by using big roller classification algorithm. Secondly, this study applied weighted gradient kernel(WGK) algorithm to find and include highly expected ground points from the remained candidate points. This study methods is very useful for reconstruct deformed terrain due to misclassification results by detecting and include important terrain model key points for describing ground shape at site. Especially in the case of deformed bank side of river area, this study showed highly enhanced classification and reconstruction results by using WGK algorithm.

• 한국도로학회:학술대회논문집
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• 2006.10a
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• pp.379-386
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• 2006

• Journal of Korean Society of Transportation
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• v.23 no.6 s.84
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• pp.7-18
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• 2005

Currently, design speed selection is chosen by highway function, terrain type and area type. But some standards in classifing highway function let designer decide design speed in an arbitrary manner and too rough a highway function classification system leads to a road function which can not reflect road design, and some ambiguity of terrain type leads to a road which can not reflect land use pattern. Highway design based on traffic volume level without considering area type can result high construction cost. This research paper provides new highway design standards which are based on the refinement of highway design speed selection procedure. The new design speed is summarized to be determined by a more detailed highway function, terrain type, and area type that were made considering South Korean characteristics. The new highway function is established by adopting highway function reclassification and design volumes and rural town center reclassification and new design standards for terrain type selection are developed in this research by analyzing South Korean GIS Data Base obtained over the national government offices.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.221-224
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• 2005

Hyperspectral image data have great potential to depict more detailed information on biophysical characteristics of surface materials, which are not usually available with multispectral data. This study aims to test the potential of hyperspectral data for classifying five soil classes defined by the vector product interim terrain data (VITD). In this study, we try to classify surface materials of bare soil over the study area in Korea using both hyperspectral and multispectral image data. Training and test samples for classification are selected with using VITD vector map. The spectral angle mapper (SAM) method is applied to the EO-I Hyperion data and Landsat ETM+ data, that has been radiometrically corrected and geo-rectified. Higher classification accuracy is obtained with the hyperspectral data for classifying five soil classes of gravel, evaporites, inorganic silt and sand.