• Journal of The Korean Society of Agricultural Engineers
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• v.58 no.1
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• pp.91-97
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• 2016

The objective of this study was to classify between radish and Chinese cabbage in autumn using hyperspectral images. The hyperspectral images were acquired by Compact Airborne Spectrographic Imager (CASI) with 1m spatial resolution and 48 bands covering the visible and near infrared portions of the solar spectrum from 370 to 1044 nm with a bandwidth of 14 nm. An object-based technique is used for classification of radish and Chinese cabbage. It was found that the optimum parameter values for image segmentation were scale 400, shape 0.1, color 0.9, compactness 0.5 and smoothness 0.5. As a result, the overall accuracy of classification was 90.7 % and the kappa coefficient was 0.71. The hyperspectral images can be used to classify other crops with higher accuracy than radish and Chines cabbage because of their similar characteristic and growth time.

• Superconductivity and Cryogenics
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• v.11 no.2
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• pp.23-29
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• 2009

The unique features of HTS offer opportunities and challenges to a number of applications. In this paper we focus on NMR and MRI magnets, illustrating them with the NMR/MRI magnets that we are currently and will shortly be engaged: a 1.3 GHz NMR magnet, an "annulus" magnet, and an $MgB_2$whole-body MRI magnet. The opportunities with HTS include: 1) high fields (e.g., 1.3 GHz magnet); 2) compactness (annulus magnet); and 3) enhanced stability despite liquid-helium-free operation ($MgB_2$whole-body MRI magnet). The challenges include: 1) a large screening current field detrimental to spatial field homogeneity (e.g., 1.3 GHz magnet); 2) uniformity of critical current density (annulus magnet); and 3) superconducting joints ($MgB_2$magnet).

• Progress in Superconductivity and Cryogenics
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• v.11 no.4
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• pp.1-7
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• 2009

The unique features of HTS offer Opportunities and challenges to a number of applications. In this paper we focus on NMR and MRI magnets, illustrating them with the NMR/MRI magnets that we are currently and will shortly be engaged: a 1.3GHz NMR magnet, an "annulus" magnet, and an $MgB_2$ whole-body MRI magnet. The opportunities with HTS include: 1) high fields (e.g., 1.3GHz magnet); 2) compactness (annulus magnet); and 3) enhanced stability despite liquid-helium-free operation ($MgB_2$ whole-body MRI magnet). The challenges include: 1) a large screening current Beld detrimental to spatial field homogeneity (e.g., 1.3 GHz magnet); 2) uniformity of critical current density (annulus magnet); and 3) superconducting joints ($MgB_2$ magnet).

• Journal of Forest and Environmental Science
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• v.32 no.4
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• pp.384-391
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• 2016

In this study, regions infected by pine wilt disease were extracted by using object-based classification method (OB-infected region), and the characteristics of special distribution about OB-infected region were figured out. Scale 24, Shape 0.1, Color 0.9, Compactness 0.5, and Smoothness 0.5 was selected as the objected-based, optimal weighted value of OB-infected region classification. The total accuracy of classification was high with 99% and Kappa coefficient was also high with 0.97. The area of OB-infected region was approximately 90 ha, 16% of the total area. The OB-infected region in Age class V and VI was intensively distributed with 97% of the total. Also, The OB-infected region in Middle and Large DBH class was intensively distributed with 99% of the total. In terms of the topographic characteristics of OB-infected region, the damages occurred approximately 86% below the altitude of 200 m, and occurred 91% with a slope less than 10 degree. The damage occurred a lot in low hilly mountain and undulating slope. In addition, the accessibility to road and residential area from OB-infected region was less than 300 m in large part. Overall, it was figured out that artificial effect is stronger than natural effect with regard to the spread of pine wilt disease.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.6
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• pp.91-97
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• 2015

The Unmanned Aerial Vehicles (UAVs) have several advantages over conventional RS techniques. They can acquire high-resolution images quickly and repeatedly. And with a comparatively lower flight altitude i.e. 80~400 m, they can obtain good quality images even in cloudy weather. Therefore, they are ideal for acquiring spatial data in cases of small agricultural field with mixed crop, abundant in South Korea. This paper discuss the use of low cost UAV based remote sensing for classifying crops. The study area, Gochang is produced by several crops such as red pepper, radish, Chinese cabbage, rubus coreanus, welsh onion, bean in South Korea. This study acquired images using fixed wing UAV on September 23, 2014. An object-based technique is used for classification of crops. The results showed that scale 250, shape 0.1, color 0.9, compactness 0.5 and smoothness 0.5 were the optimum parameter values in image segmentation. As a result, the kappa coefficient was 0.82 and the overall accuracy of classification was 85.0 %. The result of the present study validate our attempts for crop classification using high resolution UAV image as well as established the possibility of using such remote sensing techniques widely to resolve the difficulty of remote sensing data acquisition in agricultural sector.

• Nuclear Engineering and Technology
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• v.54 no.12
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• pp.4679-4683
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• 2022

Environmental radiation monitoring is required to protect from the effects of radiation in industrial fields such as nuclear power plant (NPP) monitoring, and various gamma camera systems are being developed. The purpose of this study is to optimize parameters of a diverging collimator composed of pure tungsten for compactness and lightness through Monte Carlo simulation. We conducted the performance evaluation based on spatial resolution and signal-to-noise ratio for point source and obtained gamma images and profiles. As a result, optimization was determined at a collimator height of 60.0 mm, a hole size of 1.5 mm, and a septal thickness of 1.0 mm. Also, the full-width-at-half-maximum was 3.5 mm and the signal-to-noise ratio was 53.5. This study proposes a compact 45° diverging collimator structure that can quickly and accurately identify the location of the source for radiation monitoring.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.1
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• pp.1-14
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• 2017

A land cover map provides basic information to help understand the current state of a region, but its utilization in the ecological research field has deteriorated due to limited temporal and spatial resolutions. The purpose of this study was to investigate the possibility of using a land cover map with data based on high resolution images acquired by UAV. Using the UAV, 10.5 cm orthoimages were obtained from the $2.5km^2$ study area, and land cover maps were obtained from object-based and pixel-based classification for comparison and analysis. From accuracy verification, classification accuracy was shown to be high, with a Kappa of 0.77 for the pixel-based classification and a Kappa of 0.82 for the object-based classification. The overall area ratios were similar, and good classification results were found in grasslands and wetlands. The optimal image segmentation weights for object-based classification were Scale=150, Shape=0.5, Compactness=0.5, and Color=1. Scale was the most influential factor in the weight selection process. Compared with the pixel-based classification, the object-based classification provides results that are easy to read because there is a clear boundary between objects. Compared with the land cover map from the Ministry of Environment (subdivision), it was effective for natural areas (forests, grasslands, wetlands, etc.) but not developed areas (roads, buildings, etc.). The application of an object-based classification method for land cover using UAV images can contribute to the field of ecological research with its advantages of rapidly updated data, good accuracy, and economical efficiency.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.4
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• pp.100-118
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• 2015

This study established a land-cover classification method on objects using satellite images, and figured out distributional patterns of land cover according to categories through spatial statistics techniques. Object-based classification generated each land cover classification map by spectral information, texture information, and the combination of the two. Through assessment of accuracy, we selected optimum land cover classification map. Also, to figure out spatial distribution pattern of land cover according to categories, we analyzed hot spots and quantified them. Optimal weight for an object-based classification has been selected as the Scale 52, Shape 0.4, Color 0.6, Compactness 0.5, Smoothness 0.5. In case of using the combination of spectral information and texture information, the land cover classification map showed the best overall classification accuracy. Particularly in case of dry fields, protected cultivation, and bare lands, the accuracy has increased about 12 percent more than when we used only spectral information. Forest, paddy fields, transportation facilities, grasslands, dry fields, bare lands, buildings, water and protected cultivation in order of the higher area ratio of DMZ according to categories. Particularly, dry field sand transportation facilities in Yanggu occurred mainly in north areas of the civilian control line. dry fields in Cheorwon, forest and transportation facilities in Inje fulfilled actively in south areas of the civilian control line. In case of distributional patterns according to categories, hot spot of paddy fields, dry fields and protected cultivation, which is related to agriculture, was distributed intensively in plains of Yanggu and in basin areas of Cheorwon. Hot spot areas of bare lands, waters, buildings and roads have similar distribution patterns with hot spot areas related to agriculture, while hot spot areas of bare lands, water, buildings and roads have different distributional patterns with hot spot areas of forest and grasslands.

• Journal of the Korea Society of Computer and Information
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• v.17 no.12
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• pp.83-93
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• 2012

This paper proposes an image segmentation framework that modifies the objective function of Fuzzy C-Means (FCM) to improve the performance and computational efficiency of the conventional FCM-based image segmentation. The proposed image segmentation framework includes a locally weighted fuzzy c-means (LWFCM) algorithm that takes into account the influence of neighboring pixels on the center pixel by assigning weights to the neighbors. Distance between a center pixel and a neighboring pixels are calculated within a window and these are basis for determining weights to indicate the importance of the memberships as well as to improve the clustering performance. We analyzed the segmentation performance of the proposed method by utilizing four eminent cluster validity functions such as partition coefficient ($V_{pc}$), partition entropy ($V_{pe}$), Xie-Bdni function ($V_{xb}$) and Fukuyama-Sugeno function ($V_{fs}$). Experimental results show that the proposed LWFCM outperforms other FCM algorithms (FCM, modified FCM, and spatial FCM, FCM with locally weighted information, fast generation FCM) in the cluster validity functions as well as both compactness and separation.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.5
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• pp.83-90
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• 2020

In this paper, a web-based seed phenotype visualization support system is proposed to extract and visualize data such as the surface color, length, area, perimeter and compactness of seed, which is phenotype information from the image of soybean/rice seeds. This system systematically stores data extracted from seeds in databases, and provides a web-based user interface that facilitates the analysis of data by researchers using data tables and charts. Conventional seed characteristic studies have been manually measured by humans, but the system developed in this paper allows researchers to simply upload seed images for analysis and obtain seed's numerical data after image processing. It is expected that the proposed system will be able to obtain time efficiency and remove spatial restriction, if it is used in seed characterization research, and it will be easy to analyze through systematic management of research results and visualization of the phenotype characteristics.