• Spatial Information Research
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• v.22 no.3
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• pp.23-24
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• 2014

NGII (National Geographic Information Institute) has been providing VRS (Virtual Reference Station) service so that could determine precise positioning in real time since 2007. However, since the VRS service has to maintain the connected status with VRS server, the number of users who can use VRS service are limited by capacity of VRS server. To solve this problem, NGII has been providing FKP (Virtual Reference Station) service using one way telecommunication from November 1, 2012. Therefore, it is predicted that the usage of FKP service will increase in public surveying and cadastral resurveying in the future. However, the studies with respect to analysis of FKP precision for applying to public surveying and cadastral resurveying is not conducted enough. In this study, to analyse the application possibility of FKP on the public surveying and cadastral resurveying, the two kind analysis were performed. First is the analysis of accuracy according to the configuration of reference station of FKP and VRS. One is consisted of same reference stations, another is consisted of different reference stations. Second is the accuracy anlalysis of horizontal and vertical positioning acquiring VRS and FKP data in various measurement environment based on VRS regulation. Result of first study, Positioning accuracy according to the configuration of the reference stations satisfies related regulation. However, accuracy of FKP in case of different reference stations is worse than in case of same reference stations.. The result of second test shows that the horizontal precision of FKP and VRS in good measurement environment satisfy the allowed precision. However, in some case, horizontal precision of FKP and VRS in poor measurement environment exceed the allowed precision. In addition, the number of exceeding the allowed precision in the FKP is more than the VRS. The vertical precision of the VRS satisfy related work provision. In conclusion, the result of this study shows that the FKP only in open area should be used for public survey and cadastral resurvey. Therefore the additional studies with respect to the improvement of FKP precision should be conducted.

• Geophysics and Geophysical Exploration
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• v.21 no.3
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• pp.171-182
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• 2018

We performed the seismic field data processing using an open-source software (Madagascar) to verify if it is applicable to processing of field data, which has low signal-to-noise ratio and high uncertainties in velocities. The Madagascar, based on Python, is usually supposed to be better in the development of processing technologies due to its capabilities of multidimensional data analysis and reproducibility. However, this open-source software has not been widely used so far for field data processing because of complicated interfaces and data structure system. To verify the effectiveness of the Madagascar software on field data, we applied it to a typical seismic data processing flow including data loading, geometry build-up, F-K filter, predictive deconvolution, velocity analysis, normal moveout correction, stack, and migration. The field data for the test were acquired in Gunsan Basin, Yellow Sea using a streamer consisting of 480 channels and 4 arrays of air-guns. The results at all processing step are compared with those processed with Landmark's ProMAX (SeisSpace R5000) which is a commercial processing software. Madagascar shows relatively high efficiencies in data IO and management as well as reproducibility. Additionally, it shows quick and exact calculations in some automated procedures such as stacking velocity analysis. There were no remarkable differences in the results after applying the signal enhancement flows of both software. For the deeper part of the substructure image, however, the commercial software shows better results than the open-source software. This is simply because the commercial software has various flows for de-multiple and provides interactive processing environments for delicate processing works compared to Madagascar. Considering that many researchers around the world are developing various data processing algorithms for Madagascar, we can expect that the open-source software such as Madagascar can be widely used for commercial-level processing with the strength of expandability, cost effectiveness and reproducibility.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.3
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• pp.199-208
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• 2019

As the number of available satellites increases and technology advances, image information outputs are becoming increasingly diverse and a large amount of data is accumulating. In this study, we propose a change detection method for high-resolution satellite images that uses transfer learning and a deep learning network to overcome the limit caused by insufficient training data via the use of pre-trained information. The deep learning network used in this study comprises convolutional layers to extract the spatial and spectral information and convolutional long-short term memory layers to analyze the time series information. To use the learned information, the two initial convolutional layers of the change detection network are designed to use learned values from 40,000 patches of the ISPRS (International Society for Photogrammertry and Remote Sensing) dataset as initial values. In addition, 2D (2-Dimensional) and 3D (3-dimensional) kernels were used to find the optimized structure for the high-resolution satellite images. The experimental results for the KOMPSAT-3A (KOrean Multi-Purpose SATllite-3A) satellite images show that this change detection method can effectively extract changed/unchanged pixels but is less sensitive to changes due to shadow and relief displacements. In addition, the change detection accuracy of two sites was improved by using 3D kernels. This is because a 3D kernel can consider not only the spatial information but also the spectral information. This study indicates that we can effectively detect changes in high-resolution satellite images using the constructed image information and deep learning network. In future work, a pre-trained change detection network will be applied to newly obtained images to extend the scope of the application.

• Journal of Practical Agriculture & Fisheries Research
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• v.23 no.1
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• pp.89-104
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• 2021

Based on the TF-IDF weighted value that evaluates the importance of words that play a key role, the semantic network analysis(SNA) was conducted on the self-introduction letter of freshman at Korea National College of Agriculture and Fisheries(KNCAF) in 2020. The top three words calculated by TF-IDF weights were agriculture, mathematics, study (Q. 1), clubs, plants, friends (Q. 2), friends, clubs, opinions, (Q. 3), mushrooms, insects, and fathers (Q. 4). In the relationship between words, the words with high betweenness centrality are reason, high school, attending (Q. 1), garbage, high school, school (Q. 2), importance, misunderstanding, completion (Q.3), processing, feed, and farmhouse (Q. 4). The words with high degree centrality are high school, inquiry, grades (Q. 1), garbage, cleanup, class time (Q. 2), opinion, meetings, volunteer activities (Q.3), processing, space, and practice (Q. 4). The combination of words with high frequency of simultaneous appearances, that is, high correlation, appeared as 'certification - acquisition', 'problem - solution', 'science - life', and 'misunderstanding - concession'. In cluster analysis, the number of clusters obtained by the height of cluster dendrogram was 2(Q.1), 4(Q.2, 4) and 5(Q. 3). At this time, the cohesion in Cluster was high and the heterogeneity between Clusters was clearly shown.

• Journal of Korea Entertainment Industry Association
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• v.15 no.3
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• pp.231-241
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• 2021

This study intended to present strategies and implications for attracting new students and customized education to university officials through research on the participation of workers' academic aspirations in university education. Thus, variables were derived by analyzing prior data, and causal settings between variables and questionnaires were developed. Subject to the survey, 331 workers interested in participating in university education were collected through interpersonal interviews. The collected data were dataized, and reliability and feasibility verification and frequency analysis were conducted. Finally, we validate the fit of the structural equation model and the causal relationship for each concept. Therefore, the results of the validation show the following implications. First, university officials should be motivated by a mentor and mentee system with experienced people who have switched to a suitable vocational group through university education. It will also be necessary to develop and disseminate programs so that they can continue to develop themselves for the future. To this end, it will be necessary to help them understand their aptitude and strengths through consultation with experts. Second, university officials should strengthen public relations so that prospective students can know the cases and information of the job transformation of the admitted workers through recommendations. It will also be necessary to develop university education programs that can self-develop, accept various ideas through "public contest", and provide accurate information about university education to workers through re-processing. Third, university officials should provide workers with a program that allows them to catch two rabbits: job transformation and self-improvement through university education. In other words, it is necessary to stimulate the motivation of workers by providing various information such as visiting advanced overseas companies, obtaining various certificates, moving between departments of blue-collar and white-collar, and transfer opportunities. Fourth, university officials should actively promote university education programs related to this by participating in university education and receiving systematic education and the flow of social environment. Finally, university officials will need to consult and promote workers so that they can self-develop when they participate in college education, and they will have to figure out what they need for self-development through demand surveys and analysis.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1505-1514
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• 2022

Image matching is a crucial preprocessing step for effective utilization of multi-temporal and multi-sensor very high resolution (VHR) satellite images. Deep learning (DL) method which is attracting widespread interest has proven to be an efficient approach to measure the similarity between image pairs in quick and accurate manner by extracting complex and detailed features from satellite images. However, Image matching of VHR satellite images remains challenging due to limitations of DL models in which the results are depending on the quantity and quality of training dataset, as well as the difficulty of creating training dataset with VHR satellite images. Therefore, this study examines the feasibility of DL-based method in matching pair extraction which is the most time-consuming process during image registration. This paper also aims to analyze factors that affect the accuracy based on the configuration of training dataset, when developing training dataset from existing multi-sensor VHR image database with bias for DL-based image matching. For this purpose, the generated training dataset were composed of correct matching pairs and incorrect matching pairs by assigning true and false labels to image pairs extracted using a grid-based Scale Invariant Feature Transform (SIFT) algorithm for a total of 12 multi-temporal and multi-sensor VHR images. The Siamese convolutional neural network (SCNN), proposed for matching pair extraction on constructed training dataset, proceeds with model learning and measures similarities by passing two images in parallel to the two identical convolutional neural network structures. The results from this study confirm that data acquired from VHR satellite image database can be used as DL training dataset and indicate the potential to improve efficiency of the matching process by appropriate configuration of multi-sensor images. DL-based image matching techniques using multi-sensor VHR satellite images are expected to replace existing manual-based feature extraction methods based on its stable performance, thus further develop into an integrated DL-based image registration framework.

• Journal of Korean Home Economics Education Association
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• v.34 no.3
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• pp.1-23
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• 2022

This study examined the perceptions of Myanmar university students and professors regarding the status and necessity of higher education programs in fashion. Data were collected from professors in textile engineering at Yangon Technological University and Myanmar university students. Closed- and open-ended questions were asked either through interviews or by email. The responses were analyzed using keyword extraction and categorization, and descriptive statistics(closed questions). Generally, the professors perceived higher education, as well as the cultural industries including art and fashion, as important for Myanmar's social and economic development. According to the students interests in pursuing a degree in textile were limited, despite the high interest in fashion. Low wages in the apparel industry and lack of fashion degrees that meet the demand of students were cited as reasons. The demand was high for educational programs in fashion product development, fashion design, pattern-making, fashion marketing, branding, management, costume history, and cultural studies. Students expected to find their future career in textiles and clothing factories. Many students wanted to be hired by global fashion brands for higher salaries and training for advanced knowledge and technical skills. They perceived advanced fashion education programs will have various positive effects on Myanmar's national economy.

• Journal of Korea Water Resources Association
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• v.56 no.6
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• pp.403-417
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• 2023

In this research, a methodology was developed for constructing an appropriate rainfall image database for estimating rainfall intensity based on CCTV video. The database was constructed in the Large-Scale Climate Environment Chamber of the Korea Conformity Laboratories, which can control variables with high irregularity and variability in real environments. 1,728 scenarios were designed under five different experimental conditions. 36 scenarios and a total of 97,200 frames were selected. Rain streaks were extracted using the k-nearest neighbor algorithm by calculating the difference between each image and the background. To prevent overfitting, data with pixel values greater than set threshold, compared to the average pixel value for each image, were selected. The area with maximum pixel variability was determined by shifting with every 10 pixels and set as a representative area (180×180) for the original image. After re-transforming to 120×120 size as an input data for convolutional neural networks model, image augmentation was progressed under unified shooting conditions. 92% of the data showed within the 10% absolute range of PBIAS. It is clear that the final results in this study have the potential to enhance the accuracy and efficacy of existing real-world CCTV systems with transfer learning.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1283-1297
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• 2023

Mid-wave infrared (MWIR) imagery, due to its ability to capture the temperature of land cover and objects, serves as a crucial data source in various fields including environmental monitoring and defense. The KOMPSAT-3A satellite acquires MWIR imagery with high spatial resolution compared to other satellites. However, the limited spatial resolution of MWIR imagery, in comparison to electro-optical (EO) imagery, constrains the optimal utilization of the KOMPSAT-3A data. This study aims to create a highly visible MWIR fusion image by leveraging the edge information from the KOMPSAT-3A panchromatic (PAN) image. Preprocessing is implemented to mitigate the relative geometric errors between the PAN and MWIR images. Subsequently, we employ a pre-trained pixel difference network (PiDiNet), a deep learning-based edge information extraction technique, to extract the boundaries of objects from the preprocessed PAN images. The MWIR fusion imagery is then generated by emphasizing the brightness value corresponding to the edge information of the PAN image. To evaluate the proposed method, the MWIR fusion images were generated in three different sites. As a result, the boundaries of terrain and objects in the MWIR fusion images were emphasized to provide detailed thermal information of the interest area. Especially, the MWIR fusion image provided the thermal information of objects such as airplanes and ships which are hard to detect in the original MWIR images. This study demonstrated that the proposed method could generate a single image that combines visible details from an EO image and thermal information from an MWIR image, which contributes to increasing the usage of MWIR imagery.