• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1343-1356
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• 2022

Clouds or shadows are the most problematic when monitoring crops using optical satellite images. To reduce this effect, a composite algorithm was used to select the maximum Normalized Difference Vegetation Index (NDVI) for a certain period. This Maximum NDVI Composite (MNC) method reduces the influence of clouds, but since only the maximum NDVI value is used for a certain period, it is difficult to show the phenomenon immediately when the NDVI decreases. As a way to maintain the spectral information of crop as much as possible while minimizing the influence of clouds, a Score-Based Composite (SBC) algorithm was proposed, which is a method of selecting the most suitable pixels by defining various environmental factors and assigning scores to them when compositing. In this study, the Sentinel-2A/B Level 2A reflectance image and cloud, shadow, Aerosol Optical Thickness(AOT), obtainging date, sensor zenith angle provided as additional information were used for the SBC algorithm. As a result of applying the SBC algorithm with a 15-day and a monthly period for Dangjin rice fields and Taebaek highland cabbage fields in 2021, the 15-day period composited data showed faster detailed changes in NDVI than the monthly composited results, except for the rainy season affected by clouds. In certain images, a spatially heterogeneous part is seen due to partial date-by-date differences in the composited NDVI image, which is considered to be due to the inaccuracy of the cloud and shadow information used. In the future, we plan to improve the accuracy of input information and perform quantitative comparison with MNC-based composite algorithm.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.511-521
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• 2022

Cloud detection and removal from satellite images is an essential process for topographic observation and analysis. Threshold-based cloud detection techniques show stable performance because they detect using the physical characteristics of clouds, but they have the disadvantage of requiring all channels' images and long computational time. Cloud detection techniques using deep learning, which have been studied recently, show short computational time and excellent performance even using only four or less channel (RGB, NIR) images. In this paper, we confirm the performance dependence of the deep learning network according to the heterogeneous learning dataset with different resolutions. The DeepLabV3+ network was improved so that channel features of cloud detection were extracted and learned with two published heterogeneous datasets and mixed data respectively. As a result of the experiment, clouds' Jaccard index was low in a network that learned with different kind of images from test images. However, clouds' Jaccard index was high in a network learned with mixed data that added some of the same kind of test data. Clouds are not structured in a shape, so reflecting channel features in learning is more effective in cloud detection than spatial features. It is necessary to learn channel features of each satellite sensors for cloud detection. Therefore, cloud detection of heterogeneous sensors with different resolutions is very dependent on the learning dataset.

• Journal of the Korean Institute of Gas
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• v.26 no.4
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• pp.27-35
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• 2022

When LPG leaks from the storage tank, the gas try to sink to the ground because LPG is heavier than air. The gas easily creates vapor clouds causing aggressive accidents in no airflow. Therefore, It is important to prevent in advance by analyzing the damaged range caused from LPG leakage and vapor clouds. So, this study analyzed the range of damaged by LPG leakage and vapor clouds with consideration of the cold air flow which is generated by the topographical characteristics and the land use status at night time in the Jeju Hagari. As a result of the cold air flow using KLAM_21, about 2 m/s of cold air was introduced in from the southeast due to the influence of the terrain. The range of damaged by LPG leakage and vapor cloud was analyzed using ALOHA. When the leak hole size is 10 cm at the wind speed of 2 m/s, the range corresponding to LEL 60 % (12,600 ppm) was 61 m which range is expected to influence in nearby residential areas. These results of this study can be used as basic data to prepare preventive measures of accidents caused by vapor cloud. Forward, it is necessary to apply CFD modeling such as FLACS to check the vapor cloud formation due to LPG leakage in a relatively narrow area and to check the cause analysis.

• The Bulletin of The Korean Astronomical Society
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• v.32 no.2
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• pp.51.1-51.1
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• 2007

• The Bulletin of The Korean Astronomical Society
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• v.32 no.2
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• pp.35.2-35.2
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• 2007

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.829-830
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• 2010

• The Bulletin of The Korean Astronomical Society
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• v.27 no.2
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• pp.29-29
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• 2002

• The Bulletin of The Korean Astronomical Society
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• v.32 no.1
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• pp.40.2-40.2
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• 2007

• The Bulletin of The Korean Astronomical Society
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• v.21
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• pp.6.2-7
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• 1996

• Journal of the Korean Society of Safety
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• v.10 no.1
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• pp.35-40
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• 1995

This study dealed with problem to estimate the uniformity of dust clouds, as a fundamental study to estimate the ignition hazardous evaluation of hybrid mixtures. The developed method was proposed to grasp space distribution of dusts, and also, an experimental apparatus considering with dispersion and reproduction of dusts were uniquely devised and studied.