• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.684-687
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• 2006

Airborne imagery must be precisely orthorectified to be used as geographical information data. GPS/INS (Global Positioning System/Inertial Navigation System) and LIDAR (LIght Detection And Ranging) data were employed to automatically orthorectify airborne images. In this study, 154 frame airborne images and LIDAR vector data were acquired. LIDAR vector data were converted to raster image for employing as reference data. To derive images with constant brightness, flat field correction was applied to the whole images. The airborne images were geometrically corrected by calculating internal orientation and external orientation using GPS/INS data and then orthorectified using LIDAR digital elevation model image. The precision of orthorectified images was validated using 50 ground control points collected in arbitrary selected five images and LIDAR intensity image. In validation results, RMSE (Root Mean Square Error) was 0.365 smaller then two times of pixel spatial resolution at the surface. It is possible that the derived mosaicked airborne image by this automatic orthorectification method is employed as geographical information data.

• 한국위성정보통신학회논문지
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• 제7권1호
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• pp.86-91
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• 2012

본 논문에서는 ETRI가 개발하여 천리안위성에 탑재한 Ka대역 안테나의 빔 커버지리 검증 결과에 대하여 기술하였다. 위성이 발사되면 위성체 및 위성에 탑재된 탑재체가 발사 환경과 우주 환경을 견디고 본래의 기능 및 성능을 발휘하는지 확인하고자 In Orbit Test(IOT)를 진행한다. ETRI는 IOT를 통하여 측정한 천리안위성 Ka대역 안테나의 방사패턴이 위성 발사 전에 지상에서 측정한 패턴과 유사한 특성을 보여주고 있음을 확인하였다. 이를 통하여 천리안위성 Ka대역 안테나의 성능이 위성의 발사 환경을 무사히 견디고 우주환경에서 당초 ETRI가 설계한 대로 정상적으로 나오고 있음이 검증되었다. IOT 후에 ETRI는 천리안위성 Ka대역 안테나의 빔 커버리지가 당초 설계한대로 잘 형성되어 있는지 확인하기 위하여 한반도 전역에서 측정차량을 이용하여 필드측정을 실시하였다. 빔 커버리지를 측정하기 위하여 기준지점인 ETRI를 포함, 한반도 외각 지역 및 제주도를 중심으로 총 17지점을 선별하여 이동 측정차량을 이용하여 필드 측정을 실시하고, 이를 지상시험(CATR) 결과와 비교 분석하였다. 분석한 결과 천리안위성 통신탑재체의 빔 커버리지가 당초 ETRI가 설계한 대로 한반도 전역에서 정상적으로 형성되어 있음을 확인할 수 있었다.

• 대한원격탐사학회지
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• 제38권1호
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• pp.103-110
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• 2022

Since satellite images generally include clouds in the atmosphere, it is essential to detect or mask clouds before satellite image processing. Clouds were detected using physical characteristics of clouds in previous research. Cloud detection methods using deep learning techniques such as CNN or the modified U-Net in image segmentation field have been studied recently. Since image segmentation is the process of assigning a label to every pixel in an image, precise pixel-based dataset is required for cloud detection. Obtaining accurate training datasets is more important than a network configuration in image segmentation for cloud detection. Existing deep learning techniques used different training datasets. And test datasets were extracted from intra-dataset which were acquired by same sensor and procedure as training dataset. Different datasets make it difficult to determine which network shows a better overall performance. To verify the effectiveness of the cloud detection network such as Cloud-Net, two types of networks were trained using the cloud dataset from KOMPSAT-3 images provided by the AIHUB site and the L8-Cloud dataset from Landsat8 images which was publicly opened by a Cloud-Net author. Test data from intra-dataset of KOMPSAT-3 cloud dataset were used for validating the network. The simulation results show that the network trained with KOMPSAT-3 cloud dataset shows good performance on the network trained with L8-Cloud dataset. Because Landsat8 and KOMPSAT-3 satellite images have different GSDs, making it difficult to achieve good results from cross-sensor validation. The network could be superior for intra-dataset, but it could be inferior for cross-sensor data. It is necessary to study techniques that show good results in cross-senor validation dataset in the future.

• 대한원격탐사학회지
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• 제29권6호
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• pp.601-610
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• 2013

As a standard water clarity variable, the vertical underwater visibility, called Secchi depth, is estimated with ocean color satellite data. In the present study, Moderate Resolvtion Imaging Spectradiometer (MODIS) data are used to measure the Secchi depth which is a useful indicator of ocean transparency for estimating the water quality and productivity. To estimate the Secchi depth $Z_v$, the empirical regression model is developed based on the satellite optical data and in-situ data. In the previous study, a semi-analytical algorithm for estimating $Z_v$ was developed and validated for Case 1 and 2 waters in both coastal and oceanic waters using extensive sets of satellite and in-situ data. The algorithm uses the vertical diffuse attenuation coefficient, $K_d$($m^{-1}$) and the beam attenuation coefficient, c($m^{-1}$) obtained from satellite ocean color data to estimate $Z_v$. In this study, the semi-analytical algorithm is validated using temporal MODIS data and in-situ data over the Yellow, Southern and East Seas including Case 1 and 2 waters. Using total 156 matching data, MODIS $Z_v$ data showed about 3.6m RMSE value and 1.7m bias value. The $Z_v$ values of the East Sea and Southern Sea showed higher RMSE than the Yellow Sea. Although the semi-analytical algorithm used the fixed coupling constant (= 6.0) transformed from Inherent Optical Properties (IOP) and Apparent Optical Properties (AOP) to Secchi depth, various coupling constants are needed for different sea types and water depth for the optimum estimation of $Z_v$.

• 대한원격탐사학회지
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• 제34권1호
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• pp.1-15
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• 2018

Passive microwave sea surface temperatures (SST) were validated in the Northwest Pacific using a total of 102,294 collocated matchup data between Global Precipitation Measurement (GPM) / GPM Microwave Sensor(GMI) data and oceanic in-situ temperature measurements from March 2014 to December 2016. A root-mean-square (RMS) error and a bias error of the GMI SST measurements were evaluated to $0.93^{\circ}C$ and $0.05^{\circ}C$, respectively. The SST differences between GMI and in-situ measurements were caused by various factors such as wind speed, columnar atmospheric water vapor, land contamination near coastline or islands. The GMI SSTs were found to be higher than the in-situ temperature measurements at low wind speed (<6 m/s) during the daytime. As the wind speed increased at night, SST errors showed positive bias. In addition, other factors, coming from atmospheric water vapor, sensitivity degradation at a low temperature range, and land contamination, also contributed to the errors. One of remarkable characteristics of the errors was their latitudinal dependence with large errors at high latitudes above $30^{\circ}N$. Seasonal characteristics revealed that the errors were most frequently observed in winter with a significant positive deviation. This implies that SST errors tend to be large under conditions of high wind speeds and low SSTs. Understanding of microwave SST errors in this study is anticipated to compensate less temporal capability of Infrared SSTs and to contribute to increase a satellite observation rate with time, especially in SST composite process.

• Journal of Astronomy and Space Sciences
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• 제28권3호
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• pp.241-252
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• 2011

A simulated network protocol provides the capability of distributed simulation to a generic simulator. Through this, full coverage of management of data and service handling among separated simulators is achieved. The distributed simulation environment is much more conducive to handling simulation load balancing and hazard treatment than a standalone computer. According to the simulated network protocol, one simulator takes on the role of server and the other simulators take on the role of client, and client is controlled by server. The purpose of the simulated network protocol is to seamlessly connect multiple simulator instances into a single simulation environment. This paper presents the development of a simulated network (simNetwork) that provides the capability of distributed simulation to a generic simulator (GenSim), which is a software simulator of satellites that has been developed by the Korea Aerospace Research Institute since 2010, to use as a flight software validation bench for future satellite development.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2007년도 Proceedings of ISRS 2007
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• pp.306-309
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• 2007

Japanese Ocean Flux Data Sets with Use of Remote Sensing Observations (J-OFURO) includes global ocean surface heat flux data derived from satellite data and are used in many studies related to air-sea interaction. Recently latent heat flux data version 2 was constructed in J-OFURO. In version 2 many points are improved compared with version 1. A bulk algorithm used for estimation of latent heat flux is changed from Kondo (1975) to COASRE 3.0(Fairall et al., 2005). In version 1 we used NCEP reanalysis data (Reynolds and Smith, 1994) as SST data. However, the temporal resolution of the data is weekly and considerably low. Recently there are many kinds of global SST data because we can obtain SST data using a microwave radiometer sensor such as TRMM/MI and Aqua/AMSR-E. Therefore, we compared many SST products and determined to use Merged satellite and in situ data Global Daily (MGD) SST provided by Japan Meteorological Agency. Since we use wind speed and specific humidity data derived from one DMSP/SSMI sensor in J-OFURO, we obtain two data at most one day. Therefore, there may be large sampling errors for the daily-mean value. In order to escape this problem, multi-satellite data are used in version 2. As a result we could improve temporal resolution from 3-days mean value in version 1 to daily-mean value in version 2. Also we used an Optimum Interpolation method to estimate wind speed and specific humidity data instead of a simple mean method. Finally the data period is extended to 1989-2004. In this presentation we will introduce latent heat flux data version 2 in J-OFURO and comparison results with other surface latent heat flux data such as GSSTF2 and HOAPS etc. Moreover, we will present validation results by using buoy data.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2007년도 Proceedings of ISRS 2007
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• pp.159-161
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• 2007

Geostationary Ocean Color Imager (GOCI), the world-first ocean remote sensing instrument on geostationary Communication, Ocean, Meteorological Satellite (COMS), will be able to take a picture of a large region several times a day (almost with every one hour interval). We, KORDI, are in charge for developing the GOCI data processing system (GDPS) which is the basic software for processing the data from GOCI. The GDPS will be based on windows operating system to produce the GOCI level 2 data products (useful for oceanographic environmental analysis) automatically in real-time mode. Also, the GDPS will be a user-interactive program by well-organized graphical user interfaces for data processing and visualization. Its products will be the chlorophyll concentration, amount of total suspended sediments (TSS), colored dissolved organic matters (CDOM) and red tide from water leaving radiance or remote sensing reflectance. In addition, the GDPS will be able to produce daily products such as water current vector, primary productivity, water quality categorization, vegetation index, using individual observation data composed from several subscenes provided by GOCI for each slit within the target area. The resulting GOCI level 2 data will be disseminated through LRIT using satellite dissemination system and through online request and download systems. This software is carefully designed and implemented, and will be tested by sub-contractual company until the end of this year. It will need to be updated in effect with respect to new/improved algorithms and the calibration/validation activities.

• 한국수자원학회논문집
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• 제55권11호
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• pp.913-922
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• 2022

본 연구에서는 하천구역에 대해 SAR 영상 분석에 의한 수면적 범위를 이용한 DEM을 개선하는 방법을 제안하고, 북한과 같은 비접근 지역에 적용 가능한 위성 기반의 3차원 하천 공간정보 구축 방법을 제시함을 목적으로 하고 있다. 이를 위해 접근 가능한 남한의 낙동강 지류인 남강 유역을 대상으로 연구를 진행하였다. 위성영상은 유럽항공우주국에서 제공하고 있는 Sentinel-1A/B 자료에 대해 2021년 1년간의 SAR 위성영상 자료를 수집하여 각 시기별의 수체면적을 추출하였다. 지상관측 수위는 WAMIS의 1시간 간격의 자료를 수집하였다. SAR영상 분석에 의해 추출한 수체면의 최저면적부터 최고면적까지 자료에 대해 지상의 계측 수위를 조합하여 수체면 변화에 따른 하천내 고도를 분석하여 DEM을 개선하였다. DEM 개선 후 하천구역내 고도가 매우 다양한 값을 나타내고 있어 기존의 DEM보다 비교적 자연스러운 형태의 하천 DEM을 구성하고 있는 것으로 판단된다. 개선된 DEM에 대한 정확도 검증을 위해 현장에서 측정한 지형 고도자료가 필요하나 자료의 부재로 인해 정확도 검증은 수행하지 못하였다. 다만 본 연구에서는 기존의 DEM과 SAR 영상 분석에 의해 분석된 수체면의 수위를 이용하여 DEM을 개선하는 방법을 제시하였다. 향후 정확도 검증에 대한 추가 연구를 수행한 후 다른 지역에 대한 적용성 검토 및 북한과 같은 미계측/비접근 지역에 적용할 수 있는 방법론을 추가로 제시하는 연구를 진행할 계획이다.

• 대한환경공학회지
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• 제28권4호
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• pp.447-452
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• 2006

대형 수체의 수질 모델 검증 효과를 향상시키기 위하여 원격탐사 기술이 적용되었다. 인공위성 영상은 대형 수체의 넓은 표면을 한꺼번에 파악할 수 있으므로 모델의 보정 및 검증에 사용되는 관측 자료의 부족함을 보완할 수 있다. 이 논문은 2000년 4월 29일과 9월 4일에 촬영된 Landsat FTM+영상을 분석하여 팔당호 표층 수온 검증 연구를 제시하고 있다. 영상으로부터 계산된 수온과 모델의 표층 수온의 자료를 획득하여 3가지 방법으로 영상에 의한 수온과 모델의 결과를 비교하였다. 4월 29일 영상의 경우 모델 결과를 기준으로 오차율이 0.13이며 9월 4일에는 오차율이 0.04로 모델의 표층 수온이 영상으로부터 계산된 수온과 잘 일치함을 알 수 있다. 그러나 영상촬영 시점의 대기의 간섭을 고려하지 못한 것이 4월 29일 결과의 오차를 발생시킨 주요 원인으로 사료된다. 그러므로 정확한 수질자료를 얻기 위해서는 영상촬영 시점의 대기의 효과를 고려한 대기보정이 필요할 것이라 사료된다.