• Bulletin of the Korean Space Science Society
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• 2009.10a
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• pp.49.3-49.3
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• 2009

Geostationary Ocean Colour Imager (GOCI) is the first ocean color instrument that will be operating in a geostationary orbit from 2010. GOCI will provide the crucial information of ocean environment around the Korean peninsula in high spatial and temporal resolutions at eight visible bands. We report an on-going development of imaging and radiometric performance prediction model for GOCI with realistic data for reflectance, transmittance, absorption, wave-front error and scattering properties for its optical elements. For performance simulation, Monte Carlo based ray tracing technique was used along the optical path starting from the Sun to the final detector plane for a fixed solar zenith angle. This was then followed by simulation of red-tide evolution detection and their radiance estimation, following the in-orbit operational sequence. The simulation results proves the GOCI flight model is capable of detecting both image and radiance originated from the key ocean phenomena including red tide. The model details and computational process are discussed with implications to other earth observation instruments.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.375-380
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• 1999

Ocean Scanning Multispectral Imager (OSMI) is a payload on the KOMPSAT satellite to perform worldwide ocean color monitoring for the study of biological oceanography. The instrument images the ocean surface using a wisk-broom motion with a swath width of 800 km and a ground sample distance (GSD) of<1km over the entire field of view (FOV). The instrument is designed to have an on-orbit operation duty cycle of 20% over the mission lifetime of 3 years with the functions of programmable gain/offset and on-board image data compression/storage. The instrument also performs sun and dark calibration for on-board instrument calibration. The OSMI instrument is a multi-spectral imager covering the spectral range from 400nm to 900nm using CCD Focal Plane Array (FPA). The ocean colors are monitored using 6 spectral channels that can be selected via ground commands. KOMPSAT satellite with OSMI was integrated and the satellite level environment tests and instrument aliveness/functional test as well, such as launch environment, on-orbit environment (Thermal/vacuum) and EMl/EMC test were performed at KARI. Test results met the requirements and the OSMI data were collected and analyzed during each test phase. The instrument is launched on the KOMPSAT satellite in the late 1999 and the image is scheduled to start collecting ocean color data in the early 2000 upon completion of on-orbit instrument checkout.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.7
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• pp.723-728
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• 2016

This study is analyzed the optical property of red tide pixel by using Landsat-7 ETM+, Landsat-8 OLI and COMS/GOCI image. In order to sample red tide pixel, Landsat-7, 8 true color image were used and obtained coordinate of red tide pixel in the true color image. Normalized water leaving radiance(nLw) and absorption coefficient were obtained from GOCI image in the same coordinate of the true color image. When red tide was not occurred the main absorption range was 412nm and 660nm but when red tide occurred it was 660nm and absorption coefficient in 412nm are drastically reduced. It made no difference of nLw spectrum between red tide pixel and non red tide pixel in nLw, but the absolute value of nLw was low than non red tide pixel, especially 660nm and 680nm wavelength sharply decrease.

• Journal of the Optical Society of Korea
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• v.5 no.1
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• pp.9-15
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• 2001

The ocean Scanning Multi-spectral Imager (OSMI) is a payload on the KOrea Multi-Purpose SATellite (KOMPSAT) to perform worldwide ocean color monitoring f the study of biological oceanography. OSMI performs solar and dark calibrations for on-orbit instrument calibration. The purpose of the solar calibration is to monitor the degradation of imaging performance for each pixel of 6 spectral bands and to correct the degradation effect on OSMI image during the ground station date processing. The design, the operation concept, and the radiometric characteristics of the solar calibration are investigated. A linear model of image response and a solar calibration radiance model are proposed to study the instrument characteristics using the solar calibration data. The performance of spectral responsivity and spatial response uniformity. The first solar calibration data and the analysis results are important references for further study on the on-orbit stability of OSMI response during its lifetime.

• Transactions of the Korean hydrogen and new energy society
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• v.29 no.5
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• pp.523-529
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• 2018

Two liquids which are generally used as fuels of rockets are mixed and their mixing process is quantitatively investigated by the use of particle image velocimetry (PIV). As working fluids for the liquid mixing, Dimethylfuran (DMF) and JetA1 oils have been used. Since the specific gravity of DMF is larger than that of JetA1 oil, the DMF oil has been set at the lower part of the JetA1 oil. For better visualization of the mixing process, Rhodamin B powder has been blended into the DMF oil. An agitator having 3 blades has been used for mixing the two liquids. For quantitative visualization, a LCD monitor has been used as a light source. A color camera, camcoder, has been used for recording the mixing process. The images captured by the camcoder have been digitized into three color components, R, G, and B. The color intensities of R, G, and B have been used as the inputs of the neural network of which hidden layer has 20 neurons. Color-to-concentration calibration has been performed before commencing the main experiments. Once this calibration is completed, the temporal changes of the concentration of the DMF has been quantitatively analyzed by using the constructed measurement system.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.218.2-218.2
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• 2012

In the remote sensing researches, the reflected bright source such as snow, cloud have effects on the image quality of wanted signal. Even though those signal from bright source are adjusted in corresponding pixel level with atmospheric correction algorithm or radiometric correction, those can be problem to the nearby signal as one of the stray light source. Especially, in the step and stare observational method which makes one mosaic image with several snap shots, one of target area can affect next to the other snap shot each other. Presented in this paper focused on the stray light analysis from unwanted reflected bright source for geostationary ocean color sensor. The stray light effect for total 16 slot images each other were performed according to 8 band filters. For the realistic simulation, we constructed system modeling with integrated ray tracing technique which realizes the same space time in the remote sensing observation among the Sun, the Earth, and the satellite. Computed stray light effect in the results of paper demonstrates the distinguishable radiance value at the specific time and space.

• Proceedings of the KSRS Conference
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• v.1
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• pp.90-93
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• 2006

The first Geostationary Ocean Color Imager (GOCI) onboard its Communication Ocean and Meteorological Satellite (COMS) is scheduled for launch in 2008. GOCI includes the eight visible-to-near-infrared (NIR) bands, 0.5km pixel resolution, and a coverage region of 2500 ${\times}$ 2500km centered at 36N and 130E. GOCI has had the scope of its objectives broadened to understand the role of the oceans and ocean productivity in the climate system, biogeochemical variables, geological and biological response to physical dynamics and to detect and monitor toxic algal blooms of notable extension through observations of ocean color. The special feature with GOCI is that like MODIS, MERIS and GLI, it will include the band triplets 660-680-745 for the measurements of sun-induced chlorophyll-a fluorescence signal from the ocean. The GOCI will provide SeaWiFS quality observations with frequencies of image acquisition 8 times during daytime and 2 times during nighttime. With all the above features, GOCI is considered to be a remote sensing tool with great potential to contribute to better understanding of coastal oceanic ecosystem dynamics and processes by addressing environmental features in a multidisciplinary way. To achieve the objectives of the GOCI mission, we develop the GOCI Data Processing System (GDPS) which integrates all necessary basic and advanced techniques to process the GOCI data and deliver the desired biological and geophysical products to its user community. Several useful ocean parameters estimated by in-water and other optical algorithms included in the GDPS will be used for monitoring the ocean environment of Korea and neighbouring countries and input into the models for climate change prediction.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.100-103
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• 2008

COMS(Communication, Ocean and Meteorological Satellite) has multiple payloads; Meteorological Image(MI), Ocean Color Imager(GOCI) and Ka-band communication payloads. MI has 4 IR and 1 visible channel. In order to improve the quality of IR image, two calibration sources are used; black body image and cold space look data. In case of COMS, the space look is performed at 10.4 degree away from the nadir in east/west direction. During space look, SUN or moon intrusions are strictly forbidden, because it would degrade the quality of collected IR channel calibration data. Therefore we shall pay attention to select space look side depending on SUN and moon location. This paper proposes and discusses a simple and complete space look side selection logic based on SUN and moon intrusion event file. Computer simulation has been performed to analyze the performance of the proposed algorithm in term of east/west angular distance between space look position and hazardous intrusion sources; SUN and moon.

• Korean Journal of Remote Sensing
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• v.37 no.5_2
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• pp.1235-1243
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• 2021

Geostationary Ocean Color Imager 2 (GOCI-II) on Geo-KOMPSAT-2 (GK2B)satellite was developed as a mission successor of GOCI on COMS which had been operated for around 10 years since launch in 2010 to observe and monitor ocean color around Korean peninsula. GOCI-II on GK2B was successfully launched in February of 2020 to continue for detection, monitoring, quantification, and prediction of short/long term changes of coastal ocean environment for marine science research and application purpose. GOCI-II had already finished IAC and IOT including early in-orbit calibration and had been handed over to NOSC (National Ocean Satellite Center) in KHOA (Korea Hydrographic and Oceanographic Agency). Radiometric calibration was periodically conducted using on-board solar calibration system in GOCI-II. The final calibrated gain and offset were applied and validated during IOT. And three video parameter sets for one day and 12 video parameter sets for a year was selected and transferred to NOSC for normal operation. Star measurement-based INR (Image Navigation and Registration) navigation filtering and landmark measurement-based image geometric correction were applied to meet the all INR requirements. The GOCI2 INR software was validated through INR IOT. In this paper, status and results of IOT, radiometric calibration and INR of GOCI-II are analysed and described.

• Ocean and Polar Research
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• v.35 no.3
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• pp.239-247
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• 2013

Digital image processing is a process to analyze a large volume of information on digital images. In this study, Artemia hatching rate was measured by automatically classifying and counting cysts and larvae based on color imaging data from cyst hatching experiments using an image processing technique. The Artemia hatching rate estimation consists of a series of processes; a step to convert the scanned image data to a binary image data, a process to detect objects and to extract their shape information in the converted image data, an analysis step to choose an optimal discriminant function, and a step to recognize and classify the objects using the function. The function to classify Artemia cysts and larvae is optimally estimated based on the classification performance using the areas and the plan-form factors of the detected objects. The hatching rate using the image data obtained under the different experimental conditions was estimated in the range of 34-48%. It was shown that the maximum difference is about 19.7% and the average root-mean squared difference is about 10.9% as the difference between the results using an automatic counting (this study) and a manual counting were compared. This technique can be applied to biological specimen analysis using similar imaging information.