• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1211-1224
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• 2023

Clouds cause many difficult problems in observing land surface phenomena using optical satellites, such as national land observation, disaster response, and change detection. In addition, the presence of clouds affects not only the image processing stage but also the final data quality, so it is necessary to identify and remove them. Therefore, in this study, we developed a new cloud detection technique that automatically performs a series of processes to search and extract the pixels closest to the spectral pattern of clouds in satellite images, select the optimal threshold, and produce a cloud mask based on the threshold. The cloud detection technique largely consists of three steps. In the first step, the process of converting the Digital Number (DN) unit image into top-of-atmosphere reflectance units was performed. In the second step, preprocessing such as Hue-Value-Saturation (HSV) transformation, triangle thresholding, and maximum likelihood classification was applied using the top of the atmosphere reflectance image, and the threshold for generating the initial cloud mask was determined for each image. In the third post-processing step, the noise included in the initial cloud mask created was removed and the cloud boundaries and interior were improved. As experimental data for cloud detection, CAS500-1 L2G images acquired in the Korean Peninsula from April to November, which show the diversity of spatial and seasonal distribution of clouds, were used. To verify the performance of the proposed method, the results generated by a simple thresholding method were compared. As a result of the experiment, compared to the existing method, the proposed method was able to detect clouds more accurately by considering the radiometric characteristics of each image through the preprocessing process. In addition, the results showed that the influence of bright objects (panel roofs, concrete roads, sand, etc.) other than cloud objects was minimized. The proposed method showed more than 30% improved results(F1-score) compared to the existing method but showed limitations in certain images containing snow.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1149-1161
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• 2021

With a trend of the utilization of computer vision for satellite images, cloud detection using deep learning also attracts attention recently. In this study, we conducted a U-Net cloud detection modeling using SPARCS (Spatial Procedures for Automated Removal of Cloud and Shadow) Cloud Dataset with the image data augmentation and carried out 10-fold cross-validation for an objective assessment of the model. Asthe result of the blind test for 1800 datasets with 512 by 512 pixels, relatively high performance with the accuracy of 0.821, the precision of 0.847, the recall of 0.821, the F1-score of 0.831, and the IoU (Intersection over Union) of 0.723. Although 14.5% of actual cloud shadows were misclassified as land, and 19.7% of actual clouds were misidentified as land, this can be overcome by increasing the quality and quantity of label datasets. Moreover, a state-of-the-art DeepLab V3+ model and the NAS (Neural Architecture Search) optimization technique can help the cloud detection for CAS500 (Compact Advanced Satellite 500) in South Korea.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.1
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• pp.13-21
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• 2021

High-resolution satellites such as Kompsat-3 and CAS-500 include optical cameras of MS (Multispectral) and PAN (Panchromatic) CCD (Charge Coupled Device) sensors installed with certain offsets. The offsets between the CCD sensors produce geometric discrepancy between MS and PAN images because a ground target is imaged at slightly different times for MS and PAN sensors. For precise pan-sharpening process, we propose a co-registration process consisting the physical sensor modeling and image matching. The physical sensor model enables the initial co-registration and the image matching is carried out for further refinement. An experiment with Kompsat-3 images produced RMSE (Root Mean Square Error) 0.2pixels level of geometric discrepancy between MS and PAN images.

• Korean Journal of Remote Sensing
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• v.37 no.4
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• pp.777-788
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• 2021

Since aerosol has a relatively short duration and significant spatial variation, satellite observations become more important for the spatially and temporally continuous quantification of aerosol. However, optical remote sensing has the disadvantage that it cannot detect AOD (Aerosol Optical Depth) for the regions covered by clouds or the regions with extremely high concentrations. Such missing values can increase the data uncertainty in the analyses of the Earth's environment. This paper presents a spatial gap-filling framework using a univariate statistical method such as DCT-PLS (Discrete Cosine Transform-based Penalized Least Square Regression) and FMM (Fast Matching Method) inpainting. We conducted a feasibility test for the hourly AOD product from AHI (Advanced Himawari Imager) between January 1 and December 31, 2019, and compared the accuracy statistics of the two spatial gap-filling methods. When the null-pixel area is not very large (null-pixel ratio < 0.6), the validation statistics of DCT-PLS and FMM techniques showed high accuracy of CC=0.988 (MAE=0.020) and CC=0.980 (MAE=0.028), respectively. Together with the AI-based gap-filling method using extra explanatory variables, the DCT-PLS and FMM techniques can be tested for the low-resolution images from the AMI (Advanced Meteorological Imager) of GK2A (Geostationary Korea Multi-purpose Satellite 2A), GEMS (Geostationary Environment Monitoring Spectrometer) and GOCI2 (Geostationary Ocean Color Imager) of GK2B (Geostationary Korea Multi-purpose Satellite 2B) and the high-resolution images from the CAS500 (Compact Advanced Satellite) series soon.

• Journal of Space Technology and Applications
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• v.3 no.3
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• pp.229-238
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• 2023

One of the important ways to improve the performance of magnetometers in satellite exploration is to reduce magnetic noise from satellites. One of the methods to decrease magnetic noise is by extending the satellite boom. However, this approach is often not preferred due to its high cost and operational considerations. Therefore, in many cases, removing interference from the satellite platform in the measured dataset is widely utilized after data acquisition. In this study, we would like to introduce an algorithm for removing magnetic noise observed from magnetometers installed on two solar panels and one main body without a boom.

• Proceedings of the Korean Society of Postharvest Science and Technology of Agricultural Products Conference
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• 2003.04a
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• pp.126-127
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• 2003

원예산물의 밀도나 비중은 내부성분, 숙도, 내부붕괴(internal breakdown)와 같은 생리장해에 큰 영향을 받기 때문에, 밀도를 측정함으로써 내부품질에 대한 간접적인 판정이 가능하다고 보고되고 있다. 이 연구는 수박의 밀도와 당도와의 상관관계를 구명하고자 수행하였으며, 이를 위해 밀도의 실시간 계측시스템을 개발하였다. 현재 농산물의 밀도를 어느 정도 측정 정밀도를 유지하면서도 신속하게 측정할 수 있는 방법은 부력법(platform scale method)이다. 이 방법은 일정 크기의 용기에 물을 가득 채운 후 대상물을 담가 배제된 물의 무게를 측정하여 밀도를 환산한다. 그러나 매번 측정할 때마다 물을 보충하고, 물을 계량해야하는 등 전처리과정이 복잡하고, 1회 측정하는데 3~5분 정도가 소요되는 단점이 있고, 또한 인력 측정시 반복간 오차가 클 것으로 예상된다. 따라서 이 연구에서는 이 같은 계측상의 번거로움을 해소하고 동시에 신속하고 반복간 측정정밀도를 높일 수 있도록 수박 밀도 실시간 계측시스템을 설계 제작하였다. 시스템은 투명아크릴 수조($\Phi$400$\times$500), 로드셀, 프레임, 채반, 전기모터, 제어장치 및 컴퓨터로 구성하였다. 밀도 계측은 인장형 로드셀(CAS SB-20L, Max. 20kg)을 사용하여 대기중에서와 수박을 완전히 물에 잠기도록 한 후 무게를 각각 측정하여 밀도를 환산하였다. 밀도 계측시스템에 이용한 AD변환기의 분해능은 12bit이고, 수박의 무게 측정범위를 4~10kg로 가정할 때 20kg 로드셀의 1 digit(1bit)로 발생되는 오차는 0.09~0.24%FS 이었고, 따라서 이 시스템의 밀도 해상도는 0.001g/㎤이하였다. 시스템 평가를 위해 탄력이 좋은 고무풍선에 수박 크기 정도로 물을 채워 고정채반에 넣고 밀도를 측정한 결과 1.002g/㎤을 나타냈다. 즉 물의 이론밀도인 1g/㎤에 근접한 값을 보여 정확한 밀도 계측이 가능한 것으로 판단되었다. 또한 밀도 계측시스템의 측정 반복간 정밀도를 파악하기 위해 수박 6개를 임으로 선정하여 3반복 측정 시험한 결과, 측정표준편차가 0.001~0.004g/㎤로 해상도보다는 다소 높았으나 대체로 양호한 결과를 나타냈다. 수박 35개를 이용하여 개발 계측시스템과 사람이 직접 부력법으로 밀도를 측정 비교한 결과, 계측시스템에 의해 측정된 수박 밀도가 사람이 측정했을 때 보다 낮게 측정되었다. 수박의 외관인자(무게, 길이, 직경, 체적), 밀도와 당도의 상관관계 구명시험을 위해 원예연구소 시험포장에서 재배된 삼복꿀수박 총 74개를 공시재료로 하였고, 시험은 출수일별로 10~14개씩 수확하여 외관인자, 밀도, 당도를 각각 측정하고, 이들 인자들간의 상관관계를 구명하였다. 외관인자들간에는 높은 상관관계를 보였으나, 외관인자들과 밀도, 외관인자들과 당도, 밀도와 당도와는 매우 낮은 상관관계를 나타냈다.

• Environmental Mutagens and Carcinogens
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• v.24 no.1
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• pp.33-39
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• 2004

To validate and to estimate the chemical hazard playa very important role to environment and human health. The detection of many synthetic chemicals including agrochemicals that may pose a genetic hazard in our environment is of great concern at present. Since these substances are not limited to the original products, and enter the environment, they have become widespread environmental pollutants, thus leading to a variety of chemicals that possibly threaten the public health. Pyrazosulfuron-ethyl [Ethyl-5-(4,6-dimethoxypyrimidin-2-ylcarbamoylsulfamoyl)-1-methylpyrazole-4-carboxylate, $C_{14}H_{18}N{6}O_{7}S,$ M.W. =414.39, CAS No. 93697-74-6], is one of well known rice herbicide belong in the sulfonyl urea group. To clarify the genotoxicity of this agrochemical, Ames bacterial reversion assay, in vitro chromosomal aberration assay with Chinese hamster lung (CHL) fibroblast and bone marrow micronucleus assay in mice were subjected. In Ames assay, although pyrazosulfuron-ethyl revealed cytotoxic at 5,000-140 $\mug/plate$ in Salmonella typhimurium TA100, no dose-dependent mutagenic potential in 4.4～70 $\mug/plate$ of S. typhimurium TA 98, TA 100, TA1535 and TA 1537 both in the absence and presence of S-9 metabolic activation system was observed. Using CHL fibroblasts, the 50% cell growth inhibition concentration $(IC_{50})$ of pyrazosulfuron-ethyl was determined as 1,243 $\mug/mL,$ and no chromosomal aberration was observed both in the absence and presence of S-9 mixture in the concentration range of 311-1,243 $\mug/mL.$ And also, in vivo micronucleus assay using mouse bone marrow, pyrazosulfuron-ethyl revealed no remarkable induction of MNPCE (micronucleated polychromatic erythrocytes/1000 polychromatic erythrocytes) in the dose range of 625-2,500 mg/kg body weight when administered orally. Consequently, Ames bacterial gene mutation with Salmonella typhimurium, in vitro chromosome aberration with mammalian cells and in vivo bone marrow micronucleus assay revealed no clastogenic potential of pyrazosulfuron-ethyl in this study.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.431-447
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• 2021

Recently, the use of high-resolution satellites is increasing in many areas. In order to supply useful satellite images stably, it is necessary to establish automatic precision geometric correction technic. Geometric correction is the process that corrected geometric errors of satellite imagery based on the GCP (Ground Control Point), which is correspondence point between accurate ground coordinates and image coordinates. Therefore, in the automatic geometric correction process, it is the key to acquire high-quality GCPs automatically. In this paper, we proposed iterative precision geometry correction method. we constructed an image pyramid and repeatedly performed GCP chip matching, outlier detection, and precision sensor modeling in each layer of the image pyramid. Through this method, we were able to acquire high-quality GCPs automatically. we then improved the performance of geometric correction of high-resolution satellite images. To analyze the performance of the proposed method, we used KOMPSAT-3 and 3A Level 1R 8 scenes. As a result of the experiment, the proposed method showed the geometric correction accuracy of 1.5 pixels on average and a maximum of 2 pixels.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.45 no.2
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• pp.161-173
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• 2019

In this study, the antioxidative and inhibitory activity of tyrosinase and collagenase for the solvent extract and silica column fractions of Artocarpus nitidus were evaluated. The activities were quantified using the 4 parameter logistic. LC/MS analysis showed that the major component of the fractions was polyphenol and the total polyphenol content of the extract was $48.1{\pm}2.6mg\;GAE/g$. The radical scavenging activities ($SC_{50}$) for 1,1-diphenyl-2-picrylhydrazyl of the extract, fraction-1 and fraction-2 were 16.7, 42.0 and $10.1{\mu}g/mL$, respectively. The value for fraction-2 was the closest to ascorbic acid ($1.5{\mu}g/mL$). The tyrosinase inhibitory activity of the extracts and the fractions showed $IC_{50}$ of 64.9, 0.9 and $1.2{\mu}g/mL$, respectively, and overall activity was higher than that of kojic acid ($7.4{\mu}g/mL$) and arbutin ($119.0{\mu}g/mL$). In the experiment by zebrafish embryo, the whitening activity of fraction-2 (27.5%) was higher than that of kojic acid (18.6%), and there was no adverse effect up to $500{\mu}g/mL$ of fraction-2. For the collagenase inhibitory activity, the samples showed $IC_{50}$ of 139.8, 20.6, and $16.8{\mu}g/mL$, respectively, which were competitive to 1, 10-Phenanthroline ($55.4{\mu}g/mL$). The extract and fraction-2 showed $IC_{50}$ of 61.8 and $67.1{\mu}g/mL$ for elastase. These results suggest that A. nitidus extract can be used as a cosmetic material useful for antioxidant, whitening, and prevention of skin aging without adverse effects.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.4
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• pp.253-265
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• 2020

As the risk of landslide is recently increasing due to the typhoons and localized heavy rains, effective techniques for the landslide damage detection are required to support the establishment of the recovery planning. This study describes the analysis of landslide-damaged areas using ISODATA(Iterative Self-Organizing Data Analysis Technique Algorithm) with Sentinel-2 image, regarding the case of Gokseong in August 7, 2020. A total of 4.75 ha of landslide-damaged areas was detected from the Sentinel-2 image using spectral characteristics of red, NIR(Near Infrared), and SWIR(Shortwave Infrared) bands. We made sure that the satellite remote sensing is an effective method to detect the landslide-damaged areas and support the establishment of the recovery planning, followed by the field surveys that require a lot of manpower and time. Also, this study can be used as a reference for the landslide management for the CAS500-1/2(Compact Advanced Satellite) scheduled to launch in 2021 and the Korean Medium Satellite for Agriculture and Forestry scheduled to launch in 2024.