• 한국컴퓨터정보학회논문지
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• 제23권2호
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• pp.17-26
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• 2018

In this paper, we propose a gait type classification method based on pressure sensor which reflects various terrain and velocity variations. In order to obtain stable gait classification performance, we divide the whole gait data into several steps by detecting the swing phase, and normalize each step. Then, we extract robust features for both topographic variation and speed variation by using the Null-LDA(Null-Space Linear Discriminant Analysis) method. The experimental results show that the proposed method gives a good performance of gait type classification even though there is a change in the gait velocity and the terrain.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2003년도 춘계학술발표회 논문집
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• pp.301-308
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• 2003

As the results of this study, the proposed method of this study which is increased to accuracy of DEM by classification of terrain is better than accuracy of DEM which is automatically generated by digital photogrammetry workstation system(DPWS). And, the edge detection method which is proposed by this study is established to extraction of geo-spatial information in ortho image.

• 전기전자학회논문지
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• 제18권4호
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• pp.536-543
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• 2014

Terrain classification is still a challenging issue in image processing, especially with high resolution satellite images. The well-known obstacles include low accuracy in the detection of targets, especially for the case of man-made structures, such as buildings and roads. In this paper, we present an efficient approach to classify and detect building footprints, foliage, grass and road from high resolution grayscale satellite images. Our contribution is to build a strong classifier using AdaBoost based on a combination of co-occurrence and Haar-like features. We expect that the inclusion of Harr-like feature improves the classification performance of the man-made structures, since Haar-like feature is extracted from corner features and rectangle features. Also, the AdaBoost algorithm selects only critical features and generates an extremely efficient classifier. Experimental result indicates that the classification accuracy of AdaBoost classifier is much higher than that of the conventional classifier using back propagation algorithm. Also, the inclusion of Harr-like feature significantly improves the classification accuracy. The accuracy of the proposed method is 98.4% for the target detection and 92.8% for the classification on high resolution satellite images.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2008년도 하계종합학술대회
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• pp.853-854
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• 2008

The terrain perception technology using passive sensors plays a key role to enhance autonomous mobility for UGV. We present an effective method to classify terrain covers based on the color information. Considering a real-time implementation, neural network is applied for the terrain classifier and wavelet features extracted from the images are used. Test results show that the proposed algorithm has a promising classification performance.

• 한국멀티미디어학회논문지
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• 제22권9호
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• pp.1122-1131
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• 2019

This paper covers research on terrain construction for unmanned aerial vehicle simulators using spatial information that was distributed by public institutions. Aerial photography, DEM, vector maps and 3D model data were used in order to create a realistic terrain simulator. A data converting method was suggested while researching, so it was generated to automatically arrange and build city models (vWorld provided) and classification methods so that realistic images could be generated by 3D objects. For example: rivers, forests, roads, fields and so on, were arranged by aerial photographs, vector map (land cover map) and terrain construction based on the tile map used by DEM. In order to verify the terrain data of unmanned aircraft simulators produced by the proposed method, the location accuracy was verified by mounting onto Unreal Engine and checked location accuracy.

• 한국군사과학기술학회지
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• 제12권4호
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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.

• 대한원격탐사학회지
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• 제25권6호
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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.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
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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.

• 제어로봇시스템학회논문지
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• 제18권3호
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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.

• 대한전자공학회논문지SP
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• 제47권4호
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• pp.28-34
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• 2010

본 논문에서는 마찰계수의 비접촉 추정을 위한 영상정보 활용방법을 제안한다. 마찰계수는 이동체의 도로주행 또는 장애물 극복에 있어 매우 중요한 요소이다. 이동체가 이동경로의 마찰계수를 미리 알 수 있다면 이동성향상을 기대할 수 있다. 본 논문의 마찰계수 추정방법은 영상정보를 활용하기 때문에 이동체가 지면과 접촉하기 전에 마찰계수를 추정 할 수 있다는 장점이 있다. 마찰계수의 비접촉 추정을 위한 영상정보 활용방법은 마찰계수측정실험과 물질그룹생성을 포함한 학습단계와 물질그룹 분류과정과 마찰계수 함수 활용을 포함한 마찰계수 추정단계로 구성되어 있으며 물질 조성비를 생성하는 영상처리는 두 단계에 모두 포함된다. 이 과정을 통해 얻은 마찰계수는 무인이동로봇이 이동경로 진입 전에 미끄러움을 판단하여 미끄럼지역을 회피 할 수 있도록 하며, 저속으로 이동이 가능한 경우 미끄럼이 발생하지 않는 적정속도를 계산하는데 확용 가능하다. 본 논문에서 사용한 지형의 마찰계수와 영상정보는 마찰계수 측정실험을 통해 취득하였다. 마찰계수 추정방법을 평가하기 위해 실험지형의 실제 마찰계수와 추정 마찰계수의 차이를 비교하였다.