• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.755-756
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• 2010

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.389-392
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• 2004

Classification of Earth natural components within a full polarimetric SAR image is one of the most important applications of radar polarimetry in remote sensing. In this paper, the unsupervised classification algorithms based on the combined use of the polarimetric processing technique such as the target decomposition and statistical complex Wishart classification method are evaluated and applied to vegetated terrain in Jeju volcanic island.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.377-378
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• 2010

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.1
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• pp.45-50
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• 2003

Texture analysis has been efficiently utilized in the area of terrain classification. In this application features have been obtained in the 2D image domain. This paper suggests 3D co-occurrence texture features by extending the concept of co-occurrence to 3D world. The suggested 3D features are described using co-occurrence histogram of digital elevations at two contiguous position as co-occurrence matrix. The practical construction of co-occurrence matrix limits the number of levels of digital elevation. If the digital elevation is quantized into the number of levels over the whole DEM(Digital Elevation Map), the distinctive features can not be obtained. To resolve the quantization problem, we employ local quantization technique which preserves the variation of elevations. Experiments has been carried out to verify the proposed 3D co-occurrence features, and the addition of the suggested features significantly improves the classification accuracy.

• Journal of IKEEE
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• v.18 no.4
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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.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.1
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• pp.56-62
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• 2011

Terrain recognition ability is crucial to the performance of legged robots in an outdoor environment. For instance, a robot will not easily walk and it will tumble or deviate from its path if there is no information on whether the walking surface is flat, rugged, tough, and slippery. In this study, the ground surface recognition ability of robots is discussed, and to enable walking robots to recognize the surface state and changes, a central moment method was used. The values of the sensor signals (load cell) of robots while walking were detected in the supported section and were analyzed according to signal variance, skewness, and kurtosis. Based on the results of such analysis, the surface state was detected and classified.

• Proceedings of the IEEK Conference
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• 2008.06a
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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.

• Journal of the Korea Society of Computer and Information
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• v.23 no.2
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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.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.04a
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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.

• Journal of Korea Multimedia Society
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• v.22 no.9
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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.