• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.12
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• pp.977-985
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• 2020

KOMPSAT-3A launched in 2015 provides Middle InfraRed(MIR) images with 3.3~5.2㎛. Though the satellite provide high resolution images for estimating bright temperature of ground objects, it is different from existing satellites developed for natural science purposes. An atmospheric compensation process is essential in order to estimate the surface brightness temperature from a single channel MIR image of KOMPSAT-3A. However, even after the atmospheric compensation process, there is a brightness temperature error due to various factors. In this paper, we analyzed the cause of the brightness temperature estimation error by tracking signal flow from camera physical characteristics to image processing. Also, we study on possibility of improvement of MIR brightness temperature bias error of KOMPSAT-3A using GEOKOMPSAT-2A. After bias compensation of a real nighttime image with a large bias error, it was confirmed that the surface brightness temperature of KOMPSAT-3A and GEOKOMPSAT-2A have correlation. We expect that the GEOKOMPSAT-2A images will be helpful to improve MIR brightness temperature bias error of KOMPSAT-3A.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.891-907
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• 2023

Compact Advanced Satellite 500-4 (CAS500-4) is scheduled to be launched to collect high spatial resolution data focusing on vegetation applications. To achieve this goal, accurate surface reflectance retrieval through atmospheric correction is crucial. Therefore, a machine learning-based atmospheric correction algorithm was developed to simulate atmospheric correction from a radiative transfer model using Sentinel-2 data that have similarspectral characteristics as CAS500-4. The algorithm was then evaluated mainly for forest areas. Utilizing the atmospheric correction parameters extracted from Sentinel-2 and GEOKOMPSAT-2A (GK-2A), the atmospheric correction algorithm was developed based on Random Forest and Light Gradient Boosting Machine (LGBM). Between the two machine learning techniques, LGBM performed better when considering both accuracy and efficiency. Except for one station, the results had a correlation coefficient of more than 0.91 and well-reflected temporal variations of the Normalized Difference Vegetation Index (i.e., vegetation phenology). GK-2A provides Aerosol Optical Depth (AOD) and water vapor, which are essential parameters for atmospheric correction, but additional processing should be required in the future to mitigate the problem caused by their many missing values. This study provided the basis for the atmospheric correction of CAS500-4 by developing a machine learning-based atmospheric correction simulation algorithm.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.345-347
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• 2015

인공위성은 다양한 센서장치, 구동장치, 전자장치들로 구성되어 있으며, 위성을 제어하는 컴퓨터 장치는 이들 장치들과 다양한 종류의 통신 버스로 연결되어있다. 정의된 프로토콜에 따라 명령을 전송하고 장치들의 상태 정보를 수집하여 위성을 운영한다. 위성에서 많이 사용되는 대부분의 상용 센서나 구동장치들은 표준화된 버스 (ex, 1553B, CAN, UART, etc ...) 인터페이스를 지원한다. 그러나 위성의 임무나 설계에 맞게 특수하게 제작된 장치의 경우는 범용의 버스보다는 용도에 적합한 프로토콜이 설계되고 제작된다. 본 논문에서는 2018년 발사 예정인 정지궤도복합위성 컴퓨터 장치 (GMU) 내의 프로세스 모듈과 시스템의 이상상태를 감지하여 GMU의 운용 모드 및 형상의 변경을 담당하는 MRE 모듈사이의 통신을 담당하는 IMGL 설계를 소개하고 IMGL 운영을 담당하는 소프트웨어 설계 및 구현 내용을 기술한다.