• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.351-361
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• 2024

Satellite imagery contains various elements such as clouds, cloud shadows, and terrain shadows. Accurately identifying and eliminating these factors that complicate satellite image analysis is essential for maintaining the reliability of remote sensing imagery. For this reason, satellites such as Landsat-8, Sentinel-2, and Compact Advanced Satellite 500-1 (CAS500-1) provide Usable Data Masks(UDMs)with images as part of their Analysis Ready Data (ARD) product. Precise detection of clouds and their shadows is crucial for the accurate construction of these UDMs. Existing cloud and their shadow detection methods are categorized into threshold-based methods and Artificial Intelligence (AI)-based methods. Recently, AI-based methods, particularly deep learning networks, have been preferred due to their advantage in handling large datasets. This study aims to analyze the applicability of constructing UDMs for high-resolution satellite images through deep learning-based cloud and their shadow detection using open-source datasets. To validate the performance of the deep learning network, we compared the detection results generated by the network with pre-existing UDMs from Landsat-8, Sentinel-2, and CAS500-1 satellite images. The results demonstrated that high accuracy in the detection outcomes produced by the deep learning network. Additionally, we applied the network to detect cloud and their shadow in KOMPSAT-3/3A images, which do not provide UDMs. The experiment confirmed that the deep learning network effectively detected cloud and their shadow in high-resolution satellite images. Through this, we could demonstrate the applicability that UDM data for high-resolution satellite imagery can be constructed using the deep learning network.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2008.11a
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• pp.195-195
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• 2008

CAS계 유리에 $CaCO_3-Al_2O_3$ 혼합물 및 화합물을 10, 30 wt% 첨가하여 저온 소걸 및 마이크로파 유전 특성을 고찰하였다. CAS계 유리의 연화온도는 $841^{\circ}C$ 이며, CAS계 유리에 $CaCO_3$ 와 30 wt%의 $CaCO_3-Al_2O_3$ 혼합물을 melting되며, 10 wt%의 $CaCO_3$, $Al_2O_3$, $1CaCO_3-1Al_2O_3$ 혼합물 및 $CaAl_2O_4$ 화합물를 10 wt% 첨가하였을 때 $900^{\circ}C$ 이하에서 소걸이 가능하였다. 복합체의 XRD 상 분석 결과, CaCO3를 첨가하였을 때에는 모든 조성이 비정질을 나타내었고, $Al_2O_3$와 $1CaCO_3-1Al_2O_3$ 혼합물은 $Al_2O_3$ 결정상이 생성되었고, $CaAl_2O_4$ 화합물은 $CaAl_2Si_2O_8$의 hexagonal와 anorthite 결정상이 생성되었다. 따라서 CAS-10 (A, C-A, CA) 복합체는 $900^{\circ}C$에서 각각 유전율 ($\varepsilon_r$) 6.4, 6.9, 5.15 와 품질계수 ($Q^*f$) 2,400, 1,500, 3,000의 마이크로파 유전 특성을 나타내어 LTCC 기판 재료로 사용이 가능하며, 특히 $CaAl_2O_4$ 화합물을 사용하였을 때 가장 우수한 유전 특성을 나타내는 것을 확인하였다.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1043-1059
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• 2021

Forest and agricultural land are very important factors in the environmental ecosystem and securing food resources. Forest and agricultural land should be monitored regularly. CAS500-4 data are expected to be effectively used as a supplement of monitoring forest and agricultural land. Prior to the launch of the CAS500-4, the relative canopy height error analysis of the digital elevation model on South Korea was performed to determine the vertical target accuracy. Especially, by considering area of interest of the CAS500-4 (mountainous or agricultural area), it is conducted that vertical error analysis according to the slope and canopy. For Gongju, Jeju, and Samcheok, the average root mean squared differences were calculated compared to the drone LiDAR digitalsurface models, which were filmed in autumn and winter and the 5 m digital elevation model from the National Geographic Information Institute. As a result, the Shuttle radar topography mission digital elevation model showed a root mean squared differences of about 8.35, 8.19, and 7.49 m, respectively, while the Copernicus digital elevation model showed a root mean squared differences of about 5.65, 6.73, and 7.39 m, respectively. In addition, the root mean squared difference of shuttle radar topography mission digital elevation model and the Copernicus digital elevation model according to the slope angle were estimated on South Korea compared to the 5 m digital elevation model from the National Geographic Information Institute. At the slope angle of between 0° to 5°, root mean squared differences of the Shuttle radar topography mission digital elevation model and the Copernicus digital elevation model showed 3.62 and 2.52 m, respectively. On the other hands root mean squared differences of the Shuttle radar topography mission digital elevation model and the Copernicus digital elevation model respectively showed about 10.16 and 11.62 m at the slope angle of 35° or higher.

• Journal of Microbiology and Biotechnology
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• v.23 no.6
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• pp.811-817
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• 2013

The present study dealt with the isolation, identification and enzyme characterization of potential luminous bacteria from water, sediment, squid, and cuttle fish samples of the Karaikal coast, Bay of Bengal, India during the study period September 2007 - August 2008. Bioluminescent strains were screened in SWC agar and identified using biochemical tests. As Shewanella henadai was found to be the most common and abundant species with maximum light emission [69,702,240 photons per second (pps)], the optimum ranges of various physicochemical parameters that enhance the luciferase activity in Shewanella hanedai were worked out. The maximum luciferase activity was observed at the temperature of $25^{\circ}C$ (69,674,387 pps), pH of 8.0 (70,523,671 pps), salinity of 20 ppt (71,674,387 pps), incubation period of 16 h (69,895,714 pps), 4% peptone (70,895,152 pps) as nitrogen source, 0.9% glycerol (71,625,196 pps), and the ionic supplements of 0.3% $CaCO_3$ (73,991,591 pps), 0.3% $K_2HPO_4$ (73,919,915 pps), and 0.2% $MgSO_4$ (72,161,155 pps). Shewanella hanedai was cultured at optimum ranges for luciferase enzyme characterization. From the centrifuged supernatant, the proteins were precipitated with 60% ammonium sulfate, dialyzed, and purified using anion-exchange chromatography, and then luciferase was eluted with 500 mM phosphate of pH 7.0. The purified luciferase enzyme was subjected to SDS-PAGE and the molecular mass was determined as 78 kDa.

• Korean Journal of Remote Sensing
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• v.36 no.5_2
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• pp.867-879
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• 2020

As the era of space technology utilization is approaching, the launch of CAS (Compact Advanced Satellite) 500-1/2 satellites is scheduled during 2021 for acquisition of high-resolution images. Accordingly, the increase of image usability and processing efficiency has been emphasized as key design concepts of the CAS 500-1/2 ground station. In this regard, "CAS 500-1/2 Image Acquisition and Utilization Technology Development" project has been carried out to develop core technologies and processing systems for CAS 500-1/2 data collecting, processing, managing and distributing. In this paper, we introduce the results of the above project. We developed an operation system to generate precision images automatically with GCP (Ground Control Point) chip DB (Database) and DEM (Digital Elevation Model) DB over the entire Korean peninsula. We also developed the system to produce ortho-rectified images indexed to 1:5,000 map grids, and hence set a foundation for ARD (Analysis Ready Data)system. In addition, we linked various application software to the operation system and systematically produce mosaic images, DSM (Digital Surface Model)/DTM (Digital Terrain Model), spatial feature thematic map, and change detection thematic map. The major contribution of the developed system and technologies includes that precision images are to be automatically generated using GCP chip DB for the first time in Korea and the various utilization product technologies incorporated into the operation system of a satellite ground station. The developed operation system has been installed on Korea Land Observation Satellite Information Center of the NGII (National Geographic Information Institute). We expect the system to contribute greatly to the center's work and provide a standard for future ground station systems of earth observation satellites.

• Journal of Satellite, Information and Communications
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• v.12 no.3
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• pp.1-7
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• 2017

Climate change adaptation must be prepared, because the pattern of climate change in Korea is higher than the global average. In particular, it is estimated that Korea's economic loss due to climate change will reach 2,800 trillion won, and at least 300 trillion won will be needed for adaptation to climate change(KEI, 2011). Accurate climate change forecasts and impact forecasts are essential for efficient use of enormous climate change adaptation costs. For this climate change prediction and impact analysis, it is necessary to grasp not only the global average concentration but also the inhomogeneity of the greenhouse gas concentration which appears in each region. In this study, we analyze the feasibility of developing a greenhouse gas observation satellite, which is a cause of climate change, and present a development plan for a low orbit environmental satellite by examining the current status of the operation of the greenhouse gas observation satellite. The GHG monitoring satellite is expected to expand the scope of environmental monitoring by water/soil/ecology in addition to climate change, along with weather/agriculture/soil observation satellites.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1061-1067
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• 2023

In this letter, we simulated an atmospheric correction for Sentinel-2 images, of which spectral bands are similar to Compact Advanced Satellite 500-4 (CAS500-4). Using the second simulation of the satellite signal in the solar spectrum - vector (6SV)2.1 radiation transfer model and random forest (RF), a type of machine learning, we developed an RF-based atmospheric correction model to simulate 6SV2.1. As a result, the similarity between the reflectance calculated by 6SV2.1 and the reflectance predicted by the RF model was very high.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1445-1462
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• 2022

Compact Advanced Satellite 500-4 (CAS500-4), which is scheduled to be launched in 2025, is a mid-resolution satellite with a 5 m resolution developed for wide-area agriculture and forest observation. To utilize satellite images, it is important to establish a precision sensor model and establish accurate geometric information. Previous research reported that a precision sensor model could be automatically established through the process of matching ground control point (GCP) chips and satellite images. Therefore, to improve the geometric accuracy of satellite images, it is necessary to improve the GCP chip matching performance. This paper proposes an improved GCP chip matching scheme for improved precision sensor modeling of mid-resolution satellite images. When using high-resolution GCP chips for matching against mid-resolution satellite images, there are two major issues: handling the resolution difference between GCP chips and satellite images and finding the optimal quantity of GCP chips. To solve these issues, this study compared and analyzed chip matching performances according to various satellite image upsampling factors and various number of chips. RapidEye images with a resolution of 5m were used as mid-resolution satellite images. GCP chips were prepared from aerial orthographic images with a resolution of 0.25 m and satellite orthogonal images with a resolution of 0.5 m. Accuracy analysis was performed using manually extracted reference points. Experiment results show that upsampling factor of two and three significantly improved sensor model accuracy. They also show that the accuracy was maintained with reduced number of GCP chips of around 100. The results of the study confirmed the possibility of applying high-resolution GCP chips for automated precision sensor modeling of mid-resolution satellite images with improved accuracy. It is expected that the results of this study can be used to establish a precise sensor model for CAS500-4.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1463-1478
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• 2022

Maritime transport accounts for 99.7% of the exports and imports of the Republic of Korea; therefore, developing a vessel monitoring system for efficient operation is of significant interest. Several studies have focused on tracking and monitoring vessel movements based on automatic identification system (AIS) data; however, ships without AIS have limited monitoring and tracking ability. High-resolution optical satellite images can provide the missing layer of information in AIS-based monitoring systems because they can identify non-AIS vessels and small ships over a wide range. Therefore, it is necessary to investigate vessel monitoring and small vessel classification systems using high-resolution optical satellite images. This study examined the possibility of developing ship monitoring systems using Compact Advanced Satellite 500-1 (CAS500-1) satellite images by first training a deep learning model using satellite image data and then performing detection in other images. To determine the effectiveness of the proposed method, the learning data was acquired from ships in the Yellow Sea and its major ports, and the detection model was established using the You Only Look Once (YOLO) algorithm. The ship detection performance was evaluated for a domestic and an international port. The results obtained using the detection model in ships in the anchorage and berth areas were compared with the ship classification information obtained using AIS, and an accuracy of 85.5% and 70% was achieved using domestic and international classification models, respectively. The results indicate that high-resolution satellite images can be used in mooring ships for vessel monitoring. The developed approach can potentially be used in vessel tracking and monitoring systems at major ports around the world if the accuracy of the detection model is improved through continuous learning data construction.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1215-1227
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• 2021

Surface reflectance, as a product of the absolute atmospheric correction process of low-orbit satellite imagery, is the basic data required for accurate vegetation analysis. The Commission on Earth Observation Satellite (CEOS) has conducted research and guidance to produce analysis-ready data (ARD) on surface reflectance products for immediate use by users. However, this trend is still in the early stages of research dealing with ARD for high-resolution multispectral images such as KOMPSAT-3A and CAS-500, as it targets medium- to low-resolution satellite images. This study first summarizes the types of distribution of ARD data according to existing cases. The link between Open Data Cube (ODC), the cloud-based satellite image application platforms, and ARD data was also explained. As a result, we present practical ARD deployment steps for high-resolution satellite images and several types of application models in the conceptual level for high-resolution satellite images deployed in ODC and cloud environments. In addition, data pricing policies, accuracy quality issue, platform applicability, cloud environment issues, and international cooperation regarding the proposed implementation and application model were discussed. International organizations related to Earth observation satellites, such as Group on Earth Observations (GEO) and Committee on Earth Observation Satellites (CEOS), are continuing to develop system technologies and standards for the spread of ARD and ODC, and these achievements are expanding to the private sector. Therefore, a satellite-holder country looking for worldwide markets for satellite images must develop a strategy to respond to this international trend.