• Journal of Korean Library and Information Science Society
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• v.23
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• pp.1-27
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• 1995

The purpose of this study is twofold: to investigate into general characteristics of the networks in Korea as a new information technology and to discuss general directions of development of the use of the Internet. This study is designed to achieve the purpose by gathering and analysing data related to the use of Internet of librarians those who work in public libraries and research and development libraries and university libraries. The major conclusions made in this study is summarized as follows. (1) From this survey, received detailed response from 69 librarians, the majority (42) from research and development libraries. The majority (56) were from Library and Information Science subject area, half of them (37) hold advanced degrees. (2) Majority (40) have accessed Internet for one year or less, 9(17%) respondents for two years, 17(32%) spend every day Internet related activity. (3) 44.9% of the respondents taught themselves. 28.9% learned informally from a colleague. Formal training from a single one-hour class to more structured learning was available to 30.4%. (4) The most common reason respondents use the Internet are to access remote database searching(73.9%), to communicate with colleagues and friends and electronic mail(52.2%), to transfer files and data exchange(36.2%), to know the current research front(23.2%). They search OPACs for a variety of traditional task-related reasons(59.4%) and to see what other libraries are doing with their automated systems(31.9%). (5) Respondents for the most part use the functions : WWW (68. 1%), E-Mail(59.4%), FTP(52.2%), Gopher(34.8%), Wais(7.2%). (6) Respondents mentioned the following advantages : access to remote log-in database, an excellent and swift communications vehicle, reduced telecommunication cost, saving time. (7) Respondents mentioned the following disadvantages : low speed of communication, difficult of access to the relevant information and library materials, and shortage of database be distributed within Korea.

• Korean Journal of Remote Sensing
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• v.36 no.1
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• pp.67-81
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• 2020

The rapid climatic changes being caused by global warming are resulting in abnormal weather conditions worldwide, which in some regions have increased the frequency of landslides. This study was aimed to analyze and compare the landslide susceptibility using the Frequency Ratio (FR), Statistical Index, Weight of Evidence, Certainty Factor, and Index of Entropy (IoE) at Woomyeon Mountain in South Korea. Through the construction of a landslide inventory map, 164 landslide locations in total were found, of which 50 (30%) were reserved to validate the model after 114 (70%) had been chosen at random for model training. The sixteen landslide conditioning factors related to topography, hydrology, pedology, and forestry factors were considered. The results were evaluated and compared using relative operating characteristic curve and the statistical indexes. From the analysis, it was shown that the FR and IoE models were better than the other models. The FR model, with a prediction rate of 0.805, performed slightly better than the IoE model with a prediction rate of 0.798. These models had the same sensitivity values of 0.940. The IoE model gave a specific value of 0.329 and an accuracy value of 0.710, which outperforms the FR model which gave 0.276 and 0.680, respectively, to predict the spatial landslide in the study area. The generated landslide susceptibility maps can be useful for disaster and land use planning.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.727-731
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• 2003

ASTER image has some advantages for classification such as 15 spectral bands and 15m ${\sim}$ 90m spatial resolution. However, in the classification using general remote sensing image, shadow areas are often classified into water area. It is very difficult to divide shadow and water. Because reflectance characteristics of water is similar to characteristics of shadow. Many land cover items are consisted in one pixel which is 15m spatial resolution. Nowadays, very high resolution satellite image (IKONOS, Quick Bird) and Digital Surface Model (DSM) by air borne laser scanner can also be used. In this study, mixed pixel analysis of ASTER image has carried out using IKONOS image and DSM. For mixed pixel analysis, high accurated geometric correction was required. Image matching method was applied for generating GCP datasets. IKONOS image was rectified by affine transform. After that, one pixel in ASTER image should be compared with corresponded 15×15 pixel in IKONOS image. Then, training dataset were generated for mixed pixel analysis using visual interpretation of IKONOS image. Finally, classification will be carried out based on Linear Mixture Model. Shadow extraction might be succeeded by the classification. The extracted shadow area was validated using shadow image which generated from 1m${\sim}$2m spatial resolution DSM. The result showed 17.2% error was occurred in mixed pixel. It might be limitation of ASTER image for shadow extraction because of 8bit quantization data.

• Korean Journal of Remote Sensing
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• v.30 no.5
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• pp.607-616
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• 2014

This research is to observe the shoreline changes in Jinha beach over the 50 years with aerial photographs and satellite images. The shoreline image feature was retrieved from the corrected images using wet and dry techniques and analyzed by DSAS from the statistical point of view. From 1967 to 1992, the mouth of Hoeya River was severely blocked and the northern shoreline off Jinha beach was eroded. The blockade of river mouth seemed to have been eased along with the completion of the dike, but soil continued to be deposited along the high sea away from the river month. Compared to the past, a layer of sediment has been formed off the northern coastline while the southern coastline has eroded. At least in the region subject to this research, the construction of a training dike is to blame. On top of that, a mere combination of dredges and artificial nourishment is not enough to take under control the changing shorelines properly. Thus, it is necessary to devise a more fundamental solution by taking into account reasons behind sediment from the river area that could change the shorelines besides the costal environment.

• Journal of KIISE:Software and Applications
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• v.32 no.7
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• pp.674-682
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• 2005

This paper proposes classification of multi-spectral satellite image based on fusion of fuzzy G-K (Gustafson-Kessel) algorithm and PCM algorithm. The suggested algorithm establishes the initial cluster centers by selecting training data from each category, and then executes the fuzzy G-K algorithm. PCM algorithm perform using classification result of the fuzzy G-K algorithm. The classification categories are allocated to the corresponding category when the results of classification by fuzzy G-K algorithm and PCM algorithm belong to the same category. If the classification result of two algorithms belongs to the different category, the pixels are allocated by Bayesian maximum likelihood algorithm. Bayesian maximum likelihood algorithm uses the data from the interior of the average intracluster distance. The information of the pixels within the average intracluster distance has a positive normal distribution. It improves classification result by giving a positive effect in Bayesian maximum likelihood algorithm. The proposed method is applied to IKONOS and Landsat TM remote sensing satellite image for the test. As a result, the overall accuracy showed a better outcome than individual Fuzzy G-K algorithm and PCM algorithm or the conventional maximum likelihood classification algorithm.

• Korean Journal of Remote Sensing
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• v.20 no.6
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• pp.419-425
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• 2004

This study has proposed a data fusion method based on the Dempster-Shafer evidence theory The Dempster-Shafer fusion uses mass functions obtained under the assumption of class-independent Gaussian assumption. In the Dempster-Shafer approach, uncertainty is represented by 'belief interval' equal to the difference between the values of 'belief' function and 'plausibility' function which measure imprecision and uncertainty By utilizing the Dempster-Shafer scheme to fuse the data from multiple sensors, the results of classification can be improved. It can make the users consider the regions with mixed classes in a training process. In most practices, it is hard to find the regions with a pure class. In this study, the proposed method has applied to the KOMPSAT-EOC panchromatic image and LANDSAT ETM+ NDVI data acquired over Yongin/Nuengpyung. area of Kyunggi-do. The results show that it has potential of effective data fusion for multiple sensor imagery.

• Korean Journal of Remote Sensing
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• v.15 no.3
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• pp.183-194
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• 1999

The Brightness, color and texture included in a multispectral satellite data are used as important factors to analyze and to apply the image data for a proper use. One of the most significant process in the satellite data analysis using texture or color information is to extract features effectively expressing the information of original image. It was described in this paper that six features were introduced to extract useful features from the analysis of the satellite data, and also a classification network using the back-propagation neural network was constructed to evaluate the classification ability of each vector feature in SPOT imagery. The vector features were adopted from the training set selection for the interesting region, and applied to the classification process. The classification results showed that each vector feature contained many merits and demerits depending on each vector's characteristics, and each vector had compatible classification ability. Therefore, it is expected that the color and texture features are effectively used not only in the classification process of satellite imagery, but in various image classification and application fields.

• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.117-136
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• 2019

In this study, a hybrid-type of day fog detection algorithm (DFDA) was developed based on the optical and textural characteristics of fog top, using the Himawari-8 /Advanced Himawari Imager data. Supplementary data, such as temperatures of numerical weather prediction model and sea surface temperatures of operational sea surface temperature and sea ice analysis, were used for fog detection. And 10 minutes data from visibility meter from the Korea Meteorological Administration were used for a quantitative verification of the fog detection results. Normalized albedo of fog top was utilized to distinguish between fog and other objects such as clouds, land, and oceans. The normalized local standard deviation of the fog surface and temperature difference between fog top and air temperature were also assessed to separate the fog from low cloud. Initial threshold values (ITVs) for the fog detection elements were selected using hat-shaped threshold values through frequency distribution analysis of fog cases.And the ITVs were optimized through the iteration method in terms of maximization of POD and minimization of FAR. The visual inspection and a quantitative verification using a visibility meter showed that the DFDA successfully detected a wide range of fog. The quantitative verification in both training and verification cases, the average POD (FAR) was 0.75 (0.41) and 0.74 (0.46), respectively. However, sophistication of the threshold values of the detection elements, as well as utilization of other channel data are necessary as the fog detection levels vary for different fog cases(POD: 0.65-0.87, FAR: 0.30-0.53).

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.279-288
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• 2019

We evaluated the land cover classification performance of SegNet, which features semantic segmentation of aerial imagery. We selected four semantic classes, i.e., urban, farmland, forest, and water areas, and created 2,000 datasets using aerial images and land cover maps. The datasets were divided at a 8:2 ratio into training (1,600) and validation datasets (400); we evaluated validation accuracy after tuning the hyperparameters. SegNet performance was optimal at a batch size of five with 100,000 iterations. When 200 test datasets were subjected to semantic segmentation using the trained SegNet model, the accuracies were farmland 87.89%, forest 87.18%, water 83.66%, and urban regions 82.67%; the overall accuracy was 85.48%. Thus, deep learning-based semantic segmentation can be used to classify land cover.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.5
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• pp.157-162
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• 2019

Recently, the awareness of S/W education, which was confined to the profession, is changing due to the changing industrial environment based on ICT technology World main countries invest competitively in S/W education and the target age group is getting lower Among them, the unplugged coding method using the robot platform is known as one of the most effective S/W training methods targeting the elementary age by the intuitive coding method and the robot platform feedback. However, the unplugged coding method using the robot platform has a disadvantage that it can not configure various interfaces for complicated coding due to limitations of H/W. In this paper, we have proposed an unplugged coding system which can input various commands for robot control by IR remote control as an interface and minute signals using robot sensor.