• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.575-578
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• 2005

Topographical images, in case of aerial or satellite images, are usually similar in colors and textures, and complex in shapes. Thus we have to use shape features of images for efficiently retrieving a query image from topographical image databases. In this paper, we propose a shape feature extraction method which is suitable for topographical images. This method, which improves the existing projection in the Cartesian coordinates, performs the projection operation in the polar coordinates. This method extracts three attributes, namely the number of region pixels, the boundary pixel length of the region from the centroid, the number of alternations between region and background, along each angular direction of the polar coordinates. It extracts the features of complex shape objects which may have holes and disconnected regions. An advantage of our method is that it is invariant to rotation/scale/translation of images. Finally we show the advantages of our method through experiments by comparing it with CSS which is one of the most successful methods in the area of shape feature extraction

• Journal of KIISE:Databases
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• v.33 no.4
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• pp.384-395
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• 2006

Topographical images such as aerial and satellite images are usually similar with respect to colors and textures but not in shapes. Thus shape features of the images and the methods of extracting them become critical for effective image retrieval from topographical image databases. In this paper, we propose a shape feature extraction method for topographical image retrieval. The method extracts a set of attributes which can model the presence of holes and disconnected regions in images and is tolerant to pre-processing, more specifically segmentation, errors. Various experiments suggest that retrieval using attributes extracted using the proposed method performs better than using existing shape feature extraction methods.

• Korean Journal of Ecology and Environment
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• v.49 no.3
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• pp.236-243
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• 2016

It was a case study to use as a basic data for efficient the preservation and management of subalpine coniferous forest in national parks. It is based on inhabitation condition of 210 individuals of Abies koreana Wilson that was found through local investigation in the sub-alpine zone of Jirisan National Park and Songnisan National Park. It analyzed the effect of the geographical location and topographical features, which are the basics of location environment, on the growth of A. koreana. The variables related to the growth of A. koreana are tree height and diameter at breast height. Topographical features include geographical longitude, altitude above sea level, slope of the mountains, aspect that describes the direction in which a slope faces and topographical wetness index. Topographical features were extracted through GIS spatial analysis. It used canonical correlation analysis to estimate whether the two variables groups have related to each other and how much they are related, if any, and estimated the effect of the geographical and topographical features on the growth structure of A. koreana using multiple regression analysis. The tree height and diameter at breast height that represent the growth structure of A. koreana show greater relation to geographical latitude distribution than topographical feature and the geographical and topographical factors show greater relation to diameter at breast height than tree height. The growth structure's variable and geographical and topographical variable of A. koreana have meaningful relation and the result shows that geographical and topographical variables explain 18.1% of the growth structure. The variables that affect the diameter at breast height of A. koreana are geographical latitude, topographical wetness index, aspect and altitude, which are put in order of statistical significance. The higher the latitude is, the smaller the diameter at breast height. Depending on the topographical feature, it becomes bigger. The variable that affects the tree height is topographical wetness index, which was the only meaningful variable. Overall, the tree height and diameter at breast height that are related to the growth structure of A. koreana are affected by geographical and topographical feature. It showed that the geographical feature affected it the most. Especially the effect of water among the topographical features is expected to be bigger than the other topographical factors. Based on the result, it is expected that geographical and topographical feature is an important factor for the growth structure of A. koreana. Even though it considered only the geographical and topographical features and used spatial analysis data produced by GIS, the research results will be useful for investigating and researching the growth environment of coniferous forest inhabiting in sub-alpine zone of national parks and are expected to be used as basic data for establishing measures to efficiently manage and preserve evergreen needleaf tree such as A. koreana.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.1
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• pp.133-142
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• 2010

To monitor composition and change of the national land, intelligent topographical classifier which enables accurate classification of land-cover types from airborne LiDAR data is highly required. We developed a topographical classifier development support system cooperating with da1a mining tool WEKA to help users to construct accurate topographical classification systems. The topographical classifier development support system has the following functions; superposing LiDAR data upon corresponding aerial images, dividing LiDAR data into tiles for efficient processing, 3D visualization of partial LiDAR data, feature from tiles, automatic WEKA input generation, and automatic C++ program generation from the classification rule set. In addition, with dam mining tool WEKA, we can choose highly distinguishable features by attribute selection function and choose the best classification model as the result topographical classifier. Therefore, users can easily develop intelligent topographical classifier which is well fitted to the developing objectives by using the topographical classifier development support system.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.1
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• pp.154-160
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• 1995

Maps are one of the most complicated types of drawings. Drawing recognition technology is not yet sophisticated enough for automated map reading. To automatically extract a road map directly form more complicated topographical maps, a very complicated algorithm is needed, simce the image generally involves such complicated patterns as symbols, characters, residential sections, rivers,etc. This paper describes a new feature extraction method based on the human optical neural field. We apply this method to extract complete set of road segments from topographical maps. The proposed method successfully extract road segments from various areas.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.11
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• pp.1496-1505
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• 1995

Maps are one of the most complicated types of drawings. Drawing recognition technology is not yet sophisticated enough for automated map reading. To automatically extract a road map dircetly from complicated topographical maps, a very sophisticated algorithm is needed, since the image generally involvfes such complicated patterns as symbols, characters, residential sections, rivers, railroads, etc. This paper proposes a new feature extraction method based on the morphology. We apply this method to extract complete set of road segments from topographical maps. The proposed method successfully extract road segments from various areas.

• The Transactions of the Korea Information Processing Society
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• v.1 no.4
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• pp.452-460
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• 1994

Maps are one of the most complicated types of drawings. Drawing recognition technology is not yet sophisticated enough for automated map reading To automatically extract a road map directly from more complicated topographical maps, a very complicated algorithm is needed, since the image generally involves such complicated patterns as symbols, characters, residential sections, rivers, railroads, etc. This paper describes a new feature extraction method based on the human optical neural field. We apply this method to extract complete set of road segments from topographical maps. The proposed method successfully extract road segments from various areas.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.5
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• pp.527-534
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• 2011

This paper describes an analysis of topographical changes to the Eulsuk-Island at the Nakdong River Estuary using a long-term dataset of high resolution aerial images from 1983 to 2007. Ground control surveying was performed at some feature points using GPS(Global Positioning System) to accomplish AT(Aerial Triangulation) for past aerial images. Even if some still existing feature points appeared on old aerial images were used as GCPs(Ground Control Points) for past aerial images in AT, its accuracy reached at 1m level. Since then, a quantitative analysis of topographical changes was conducted on digital orthophotos produced by a series of aerial images taken by different years. The change volume of total area, construction, vegetation, buildings and roads could be extracted per each period in study area. The total area decreased from 1983 to 1992, but it has not almost changed since 1992. According to the continuous development, the area of vegetation has steadily decreased, while that of buildings and roads has generally increased. The result of this study can provide us with invaluable base data for further topographical change monitoring in Eulsuk-Island and Nakdong River estuary caused by continuous development in this area.

• Journal of the Korean GEO-environmental Society
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• v.9 no.5
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• pp.61-69
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• 2008

In general, it has been usually used the method that assess rock slope stability using stereographic projection method, limit equilibrium analysis, numerical analysis and slope stability evaluation table. Several methods for assessing the stability of rock slopes has been proposed on the basis of site investigation data. These method adopted different evaluation items and weighting factors by researchers, organization and nation. But the researches for each evaluation items were insufficient. So the effectiveness of rock slope stability evaluation items for geometrical configuration (slope height, slope direction and angle, dip and dip direction of major discontinuity, absolute value for the direction difference for slope and major discontinuities) and topographical characteristic (possibility of topographical water concentration, upper natural slope angle, slope configuration) using 315 failure and stable highway rock slope analysis data, AV (abnormal value) analysis and NMAV (normalized maximum abnormal value) analysis were evaluated.

• The Journal of the Acoustical Society of Korea
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• v.14 no.2
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• pp.101-112
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• 1995

In order to construct a feature map-based phoneme classification system for speech recognition, two procedures are usually required. One is clustering and the other is labeling. In this paper, we present a phoneme classification system based on the Kohonen's Self-Organizing Feature Map (SOFM) for clusterer and labeler. It is known that the SOFM performs self-organizing process by which optimal local topographical mapping of the signal space and yields a reasonably high accuracy in recognition tasks. Consequently, SOFM can effectively be applied to the recognition of phonemes. Besides to improve the performance of the phoneme classification system, we propose the learning algorithm combined with the classical K-mans clustering algorithm in fine-tuning stage. In order to evaluate the performance of the proposed phoneme classification algorithm, we first use totaly 43 phonemes which construct six intra-class feature maps for six different phoneme classes. From the speaker-dependent phoneme classification tests using these six feature maps, we obtain recognition rate of $87.2\%$ and confirm that the proposed algorithm is an efficient method for improvement of recognition performance and convergence speed.