• Journal of the Korea Institute of Military Science and Technology
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• v.11 no.4
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• pp.68-75
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• 2008

This paper proposes a new data hiding method for binary images using the weighting value of pixel-value differencing. The binary cover image is partitioned into non-overlapping sub-blocks and find the most suitable position to embed a secret bit for each sub-block. The proposed method calculates the weighted value for a sub-block to pivot a pixel to be changed. This improves the image quality of the stego-image. The experimental results show that the proposed method achieves a good visual quality and high capacity.

• Korean Journal of Remote Sensing
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• v.25 no.3
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• pp.233-242
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• 2009

This paper presents two different types of supervised classifiers such as support vector machine (SVM) and spectral angle mapper (SAM). The Compact Airborne Spectrographic Imager (CASI) high resolution aerial image was classified with the above two classifier. The image was classified into eight land use /land cover classes. Accuracy assessment and Kappa statistics were estimated for SVM and SAM separately. The overall classification accuracy and Kappa statistics value of the SAM were 69.0% and 0.62 respectively, which were higher than those of SVM (62.5%, 0.54).

• Korean Journal of Remote Sensing
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• v.18 no.3
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• pp.163-169
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• 2002

The land uses of Korean peninsula are very complicated and high-density. Therefore, the image classification using coarse resolution satellite images may not provide good results for the land cover classification. The purpose of this paper is to compare the classification accuracy of visual interpretation with that of digital image classification of satellite remote sensing data such as 20m SPOT and 30m TM. In this study, hybrid classification was used. Classification accuracy was assessed by comparing each classification result with reference data obtained from KOMPSAT-1 EOC imagery, air photos, and field surveys.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.11
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• pp.79-84
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• 2015

Steganography is to conceal the existence of secrete data itself. The Emulab is a framework to provide real systems and network topology that can set up at anytime by researchers. In this paper, we show that steganography techniques can be applied in the Emulab environment. Steganography methods are evaluated on a standalone and sharing environments using the color bitmap images. The cover image is divided into RGB channels and then embedded the secret data at each client. The experimental results demonstrate that execution time is better in client/server environment as cover image size is increasing.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.2
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• pp.121-135
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• 2014

Object-based classification methods provide a valid alternative to traditional pixel-based methods. This study reports the results of an object-based classification to examine land cover in the demilitarized zones(DMZs) of Inje-gun. We used land cover classes(7 classes for main category and 13 classes for sub-category) selected from the criteria by Korea Ministry of Environment. The average and standard deviation of the spectrum values, and homogeneity of GLCM were chosen to map land cover types in an hierarchical approach using the nearest neighborhood method. We then identified the distributional characteristics of land cover by considering 3 topographic characteristics (altitude, slope gradient, distance from the Southern Limited Line(SLL)) within the DMZs. The results showed that scale 72, shape 0.2, color 0.8, compactness 0.5 and smoothness 0.5 were the optimum weight values while scale, shape and color were most influenced parameters in image segmentation. The forests (92%) were main land cover type in the DMZs; the grassland(5%), the urban area (2%) and the forests (broadleaf forest: 44%, mixed forest: 42%, coniferous forest: 6%) also occupied mostly in land cover classes for sub-category. The results also showed that facilities and roads had higher density within 2 km from the SLL, while paddy, field and bare land were distributed largely outside 6 km from the SLL. In addition, there was apparent distinction in land cover by topographic characteristics. The forest had higher density at above altitude 600m and above slope gradient $30^{\circ}$ while agriculture, bare land and grass land were distributed mainly at below altitude 600m and below slope gradient $30^{\circ}$.

• Journal of the Korean Geographical Society
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• v.39 no.5 s.104
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• pp.786-803
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• 2004

These days, interests on land cover classification using not only multi-sensor data but also thematic GIS information, are increasing. Often, although we have useful GIS information for the classification, the traditional classification method like maximum likelihood estimation technique (MLE) does not allow us to use the information due to the fact that the MLE and the existing computer programs cannot handle GIS data properly. We proposed a new method for updating the image classification using multi-spectral and multi-temporal images. In this study, we have simultaneously extended the MLE to accommodate both multi-spectral images data and land cover data for land cover classification. In addition to the extended MLE method, we also have extended the empirical likelihood ratio estimation technique (LRE), which is one of non-parametric techniques, to handle simultaneously both multi-spectral images data and land cover data. The proposed procedures were evaluated using land cover map based on Landsat ETM+ images in the Anmyeon-do area in South Korea. As a result, the proposed methods showed considerable improvements in classification accuracy when compared with other single-spectral data. Improved classification images showed that the overall accuracy indicated an improvement in classification accuracy of $6.2\%$ when using MLE, and $9.2\%$ for the LRE, respectively. The case study also showed that the proposed methods enable the extraction of the area with land cover change. In conclusion, land cover classification produced through the combination of various GIS spatial data and multi-spectral images will be useful to involve complementary data to make more accurate decisions.

• Korean Journal of Remote Sensing
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• v.22 no.6
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• pp.565-574
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• 2006

Satellite images have been used to obtain land cover information that is one of important factors for hydrological analysis over a large area. In urban area, more detailed land cover data are often required for hydrological analysis because of the relatively complex land cover types. The number of land cover classes that can be classified with traditional multispectral data is usually less than the ones required by most hydrological uses. In this study, we present the capabilities of hyperspectral data (Hyperion) for the classification of hydrological land cover types in urban area. To obtain 17 classes of urban land cover defined by the USDA SCS, spectral mixture analysis was applied using eight endmembers representing both impervious and pervious surfaces. Fractional values from the spectral mixture analysis were then reclassified into 17 cover types according to the ratio of impervious and pervious materials. The classification accuracy was then assessed by aerial photo interpretation over 10 sample plots.

• Journal of the Korean Association of Geographic Information Studies
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• v.2 no.3
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• pp.60-70
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• 1999

The purpose of this study is to analyze the spatial change of water quality pollutant in the upper-stream of Kumho River basin. For this purpose, it compared with ground survey data of water quality measurement, using GIS and Landsat TM image, and then constructed a database of water quality pollutants in the watershed by Arc/Info. Also the land cover classification maps of 1985 and 1997 were prepared using maximum likelihood classification. This study detected and analysed the classified images to produce the area of land cover change per sub-basin. In addition, choropleth maps were prepared with spatial change value of water quality pollutants, and overlay analysis was carried out with weight score for each layer. The results of this study revealed that population, animals and fruit orchards were main factors in the spatial change of water pollution of Kumho River basin. The Comparision of pollutions by sub-basins showed a high pollution value in Daechang-chun and Omok -chun stream which follows through the urban area.

• Journal of the Korean Society of Clothing and Textiles
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• v.41 no.3
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• pp.446-457
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• 2017

This study surveys the usage and satisfaction of car seat covers, analyzes the satisfaction and luxuriousness of materials used and provides basic data on optimum car seat covers that improve consumer satisfaction, stability, and comfort, while driving. The survey was conducted on 150 people in their 20s to 60s with a car. Consequently, achromatic colored car seat covers were used most and the satisfaction with black was very high. Interior & exterior harmony and the pursued car image were considered important, this consumer psychology impacted the color selection for car seat covers. The satisfaction reasons were different according to materials. Genuine leather was highly regarded in interior & exterior harmony (20.8%), excellent seat sensation (17.7%), excellent tactile sensation (11.5%), and luxuriousness (8.5%). For artificial leather, interior & exterior harmony (16.5%) and easiness of stain removal (13.6%) was rated high and fabric had excellent seat sensation (12.3%) and economics (10.8%). The material, heated and ventilated device affected car seat cover satisfaction. The luxurious image of car seat covers was pursed and was perceived mainly with a sense of sight. Luxury car seat covers were mainly created with materials. Genuine leather and black car seat covers increased luxuriousness. For car seat covers, those with flexibility, excellent compressive elasticity, and thickness were perceived as luxurious.

• Journal of the Korean Association of Geographic Information Studies
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• v.6 no.3
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• pp.107-116
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• 2003

This study investigated how appropriate the classification methods based on conventional spectral characteristics are for high resolution imagery. A supervised classification mixing parametric and non-parametric rules, a method in which fuzzy theory is applied to such classification, and an unsupervised method were performed and compared to each other for accuracy. In addition, comparing the result screen-digitized through interpretation to the classification result using spectral characteristics, this study analyzed the conformity of both methods. Although the supervised classification to which fuzzy theory was applied showed the best performance, the application of conventional classification techniques to high resolution imagery had some limitations due to there being too much information unnecessary to classification, shadows, and a lack of spectral information. Consequently, more advanced techniques including integration with other advanced remote sensing technologies, such as lidar, and application of filtering or template techniques, are required to classify land cover/use or to extract useful information from high resolution imagery.