• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2010.06a
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• pp.312-313
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• 2010

초해상도 영상복원은 동일 지역에서 획득한 다수의 영상을 통해 고해상도의 영상으로 복원하는 영상처리 알고리즘 기법이다. 이 기법은 비디오 영상, 위성 영상, 의료 영상과 같이 동일지역에 대한 다수의 저해상도 영상을 획득 할 수 있는 분야에 적용이 가능하다. 본 연구에서는 세계최초의 정지궤도 해양위성인 GOCI 센서의 육상 활용도를 높이기 위한 초해상도 기법 개발을 위해 MODIS 영상을 활용한 시뮬레이션을 수행하여, GOCI 센서를 위한 효율적인 초해상도 알고리즘을 제안한다.

• Korean Journal of Remote Sensing
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• v.31 no.5
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• pp.371-384
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• 2015

Although GOCI has potential for land surface monitoring, there have been only a few cases for land applications. It might be due to the lack of reliable land products derived from GOCI data for end-users. To use for land applications, it is often essential to provide cloud-free composite over land surfaces. In this study, we proposed a cloud detection method that was very important to make cloud-free composite of GOCI reflectance and vegetation index. Since GOCI does not have SWIR and TIR spectral bands, which are very effective to separate clouds from other land cover types, we developed a multi-temporal approach to detect cloud. The proposed cloud detection method consists of three sequential steps of spectral tests. Firstly, band 1 reflectance threshold was applied to separate confident clear pixels. In second step, thick cloud was detected by the ratio (b1/b8) of band 1 and band 8 reflectance. In third step, average of b1/b8 ratio values during three consecutive days was used to detect thin cloud having mixed spectral characteristics of both cloud and land surfaces. The proposed method provides four classes of cloudiness (thick cloud, thin cloud, probably clear, confident clear). The cloud detection method was validated by the MODIS cloud mask products obtained during the same time as the GOCI data acquisition. The percentages of cloudy and cloud-free pixels between GOCI and MODIS are about the same with less than 10% RMSE. The spatial distributions of clouds detected from the GOCI images were also similar to the MODIS cloud mask products.

• Spatial Information Research
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• v.19 no.1
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• pp.29-40
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• 2011

GOCI the world first Ocean Color Imager in Geostationary Orbit, which could obtain total 8 images of the same region a day, however, its spatial resolution(500m) is not enough to use for the accurate land application, Super Resolution(SR), reconstructing the high resolution(HR) image from multiple low resolution(LR) images introduced by computer vision field. could be applied to the time-series remotely sensed images such as GOCI data, and the higher resolution image could be reconstructed from multiple images by the SR, and also the cloud masked area of images could be recovered. As the precedent study for developing the efficient SR method for GOCI images, on this research, it reproduced the simulated data under the acquisition process of the remote sensed data, and then the simulated images arc applied to the proposed algorithm. From the proposed algorithm result of the simulated data, it turned out that low resolution(LR) images could be registered in sub-pixel accuracy, and the reconstructed HR image including RMSE, PSNR, SSIM Index value compared with original HR image were 0.5763, 52.9183 db, 0.9486, could be obtained.

• Journal of the Korean Association of Geographic Information Studies
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• v.16 no.1
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• pp.36-46
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• 2013

The Geostationary Ocean Color Imager (GOCI), the first geostationary ocean color observation instrument launched in 2010 on board the Communication, Ocean, and Meteorological Satellite (COMS), has been generating the operational level 1 data. This study describes a methodology for creating the GOCI true color image and data processing software, namely the GOCI RGB maker. The algorithm uses a generic atmospheric correction and reprojection technique to produce the color composite image. Especially, the program is designed for educational purpose in a way that the region of interest and image size can be determined by the user. By distributing software to public, it would maximize the understanding and utilizing the GOCI data. Moreover, images produced from the geostationary observations are expected to be an excellent tool for monitoring environmental changes.

• Korean Journal of Remote Sensing
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• v.30 no.2
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• pp.275-292
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• 2014

The objective of this study is to determine Tasseled Cap Transformation (TCT) coefficients for the Geostationary Ocean Color Imager (GOCI). TCT is traditional method of analyzing the characteristics of the land area from multi spectral sensor data. TCT coefficients for a new sensor must be estimated individually because of different sensor characteristics of each sensor. Although the primary objective of the GOCI is for ocean color study, one half of the scene covers land area with typical land observing channels in Visible-Near InfraRed (VNIR). The GOCI has a unique capability to acquire eight scenes per day. This advantage of high temporal resolution can be utilized for detecting daily variation of land surface. The GOCI TCT offers a great potential for application in near-real time analysis and interpretation of land cover characteristics. TCT generally represents information of "Brightness", "Greenness" and "Wetness". However, in the case of the GOCI is not able to provide "Wetness" due to lack of ShortWave InfraRed (SWIR) band. To maximize the utilization of high temporal resolution, "Wetness" should be provided. In order to obtain "Wetness", the linear regression method was used to align the GOCI Principal Component Analysis (PCA) space with the MODIS TCT space. The GOCI TCT coefficients obtained by this method have different values according to observation time due to the characteristics of geostationary earth orbit. To examine these differences, the correlation between the GOCI TCT and the MODIS TCT were compared. As a result, while the GOCI TCT coefficients of "Brightness" and "Greenness" were selected at 4h, the GOCI TCT coefficient of "Wetness" was selected at 2h. To assess the adequacy of the resulting GOCI TCT coefficients, the GOCI TCT data were compared to the MODIS TCT image and several land parameters. The land cover classification of the GOCI TCT image was expressed more precisely than the MODIS TCT image. The distribution of land cover classification of the GOCI TCT space showed meaningful results. Also, "Brightness", "Greenness", and "Wetness" of the GOCI TCT data showed a relatively high correlation with Albedo ($R^2$ = 0.75), Normalized Difference Vegetation Index (NDVI) ($R^2$ = 0.97), and Normalized Difference Moisture Index (NDMI) ($R^2$ = 0.77), respectively. These results indicate the suitability of the GOCI TCT coefficients.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.1
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• pp.65-71
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• 2015

Red Tide phenomenon which happens in the southern coast of Korea gives massive damage to the fishermen who run fish farms and thereby a lot of efforts to prevent damage are made from various angles. In particular, red tide monitoring with satellite imagery can make it possible to obtain the occurrence data of red tide throughout the whole areas of the sea, which helps provide important information for establishing the preventive plans of disasters. In this regard, this study selected the South Sea of Gyeongnam Province with a view to suggesting the monitoring results with regard to the spread and reduction of the Red Tide in the middle of the day by using the GOCI Images of COMS. With this intention, it selected the region in the South Sea of Gyeongnam Province. The study results of analysis on the GOCI image data for the years of 2013(Aug. 12) and 2014 (Sep. 11) are as follows: the pattern of the Red Tide in the region of the South Sea occurred in the southern sea area of Geoje-do in the morning. It gradually spread and showed a gradual decline after reaching the top at 1 PM. In addition, in terms of the tide movement in the middle of the day, Red Tide began in the southern sea area and moved to the west, and moved to the east again at noon. It is judged that additional study on many factors such as the characteristics of the future Red-tide organisms, tidal currents, amount of sunshine, and water temperature is needed, but it is estimated that Red Tide movement monitoring with GOCI images would provide very crucial information for predicting the spread and movement of the Red Tide to protect and manage the Red Tide disasters.

• 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.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.5
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• pp.353-361
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• 2015

The area of red tides occurences, which brings enormous damages every year, have been expanded to the coastal waters across the nation. Regarding to this trend, the development of red tide detection technology by using the GOCI (Geostationary Ocean Color Imager) of COMS lauched in 2010 has been drawn attentions of researchers. This study purposed on analyzing the frequency and density of red tides occurence by using the GOCI for detecting the southern sea, whereas targeted area. The observation has brought over the last three years (2012, 2013, and 2014) before the analysis was conducted. Followingly, the study could be resulted in extracting and revealing the hot spots of the red tides from two of analysis in the overlay and density. The distribution patterns of red tide occurrences according to those observed years has been shown in irregular characteristics and various changes. However, the analysis of hot spots, based on the frequency of the red tide occurrence, has revealed that the frequency of red tide occurences is continuously increased in the specific sea area. Therefore, it is concluded in that the continuous monitoring can contribute to predict accurate movements of red tides, so as establish systematic plans for preventing disasters.

• Journal of The Korean Society of Agricultural Engineers
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• v.55 no.6
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• pp.47-55
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• 2013

The objective of this study is to evaluate the applicability of Communication, Ocean, and Meteorological Satellite (COMS) Geostationary Ocean Color Imager (GOCI) vegetation indices on a quantitative analysis. For evaluation, the vegetation indices such as RVI, NDVI and SAVI were extracted by using COMS GOCI and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) imageries. The 4,000 points using simple random sampling (SRS) method were randomly extracted from land areas except ocean to compare the vegetation indices from two images. The results of linear regression showed that the regression coefficients of RVI, NDVI, and SAVI between COMS GOCI and Terra MODIS were 0.66~0.82, 0.71~0.83, and 0.71~0.83, respectively. Especially, the regression coefficients of RVI (r=0.85), NDVI (r=0.91) and SAVI (r=0.91) were strongly related from September 2011 to January 2012. Thus, COMS GOCI can be substituted for particular periods and it needs to verify additionally.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1523-1528
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• 2023

Since the successful launch of Geostationary Ocean Color Imager-II (GOCI-II) in February 2020, various studies for improving the accuracies of the product have been underway through full-scale Cal/Val (calibration and validation) activities. This special issue examines the algorithm for GOCI-II data quality management at present, two years after the start of studies on Cal/Val and algorithm improvement of GOCI-II data, and introduces accuracy improvement and application progress along with the related research results. We expect that highly accurate data will be provided and utilized through continuous Cal/Val activities for GOCI-II data.