• Journal of The Korean Society of Grassland and Forage Science
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• v.33 no.3
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• pp.177-184
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• 2013

This work was conducted to assess the use of Near-infrared reflectance spectroscopy (NIRS) as a technique to analyze nutritional constituents of Distillers dried grain with solubles (DDGS) and corn quickly and accurately, and to apply an NIRS-based indium gallium arsenide array detector, rather than a NIRS-based scanning system, to collect spectra and induce and analyze calibration equations using equipment which is better suited to field application. As a technique to induce calibration equations, Partial Least Squares (PLS) was used, and for better accuracy, various mathematical transformations were applied. A multivariate outlier detection method was applied to induce calibration equations, and, as a result, the way of structuring a calibration set significantly affected prediction accuracy. The prediction of nutritional constituents of distillers dried grains with solubles resulted in the following: moisture ($R^2$=0.80), crude protein ($R^2$=0.71), crude fat ($R^2$=0.80), crude fiber ($R^2$=0.32), and crude ash ($R^2$=0.72). All constituents except crude fiber showed good results. The prediction of nutritional constituents of corn resulted in the following: moisture ($R^2$=0.79), crude protein ($R^2$=0.61), crude fat ($R^2$=0.79), crude fiber ($R^2$=0.63), and crude ash ($R^2$=0.75). Therefore, all constituents except for crude fat and crude fiber were predicted for their chemical composition of DDGS and corn through Near-infrared reflectance spectroscopy.

• Journal of Animal Environmental Science
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• v.20 no.3
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• pp.117-124
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• 2014

The objectives were to assess the use of near-infrared reflectance spectroscopy (NIRS) as a tool for estimating nutrient compositions of corn kernel, and to apply an NIRS-based indium gallium arsenide array detector to the system for collecting spectra and analyzing calibration equations using equipments designed for field application. Partial Least Squares Regression (PLSR) was employed to develop calibration equations based on representative data sets. The kennard-stone algorithm was applied to induce a calibration set and a validation set. As a result, the method for structuring a calibration set significantly affected prediction accuracy. The prediction of chemical composition of corn kernel resulted in the following (kennard-stone algorithm: relative) moisture ($R^2=0.82$, RMSEP=0.183), crude protein ($R^2=0.80$, RMSEP=0.142), crude fat ($R^2=0.84$, RMSEP=0.098), crude fiber ($R^2=0.74$, RMSEP=0.098), and crude ash ($R^2=0.81$, RMSEP=0.048). Result of this experiment showed the potential of NIRS to predict the chemical composition of corn kernel.

• Proceedings of the KSRS Conference
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• v.1
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• pp.71-74
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• 2006

Communication Ocean Meteorological Satellite (COMS) is planned to be launched onto Geostationary Earth Orbit in 2008. The meteorological imager (MI) is one of COMS payloads and has 5 spectral channels to monitor meteorological phenomenon around the Korean peninsular intensively and of Asian-side full Earth disk periodically. The MI has on-board radiometric calibration capabilities called 'blackbody calibration' for infrared channels and 'space look' for infrared/visible channels, and radiometric response stability monitoring device called 'albedo monitor' for visible channel. Additionally the MI has on-board function called 'electrical calibration' for the check of imaging path electronics of both infrared and visible channels. The characterization of MI performance is performed to provide the pre-launch radiometric calibration data which will be used for in-orbit radiometric calibration with the on-board calibration outputs. The radiometric calibration of the COMS MI is introduced in the view point of instrument side in terms of in-orbit calibration devices and capabilities as well as the pre-launch calibration activities and expected outputs.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.494-497
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• 2004

To date the KOMPSAT OSMI(Ocean Scanning Multi-spectral Imager) data have been widely used in natural disaster monitoring such as Typhoon, Asian Dust, Red Tide, and Forest Fire. Quantitative analyses related to the marine ecosystem have been delayed because they require good quality of data through Cal/Val activities. To resolve such problem, KARI performed the OSMI crosscalibration study with SeaWiFS team. In this study, we will demonstrate the OSMI ocean color products with updated cross-calibration coefficients and compare them to the previous cross-calibration results.

• Journal of Pharmaceutical Investigation
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• v.37 no.6
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• pp.377-395
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• 2007

Near infrared(NIR) spectral data have been used for the noninvasive analysis of various biological samples. Nonetheless, absorption bands of NIR region are overlapped extensively. It is very difficult to select the proper wavelengths of spectral data, which give the best PCR(principal component regression) models for the analysis of constituents of biological samples. The NIR data were used after polynomial smoothing and differentiation of 1st order, using Savitzky-Golay filters. To find the best PCR models, all-possible combinations of available principal components from the given NIR spectral data were derived by in-house programs written in MATLAB codes. All of the extensively generated PCR models were compared in terms of SEC(standard error of calibration), $R^2$, SEP(standard error of prediction) and SECP(standard error of calibration and prediction) to find the best combination of principal components of the initial PCR models. The initial PCR models were found by SEC or Malinowski's indicator function and a priori selection of spectral points were examined in terms of correlation coefficients between NIR data at each wavelength and corresponding concentrations. For the test of the developed program, aqueous solutions of BSA(bovine serum albumin) and glucose were prepared and analyzed. As a result, the best PCR models were found using a priori selection of spectral points and the final model selection by SEP or SECP.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.390-395
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• 1999

Ocean Scanning Multispectral Imager (OSMI) is a payload on the Korean Multi-purpose SATellite (KOMPSAT) to perform worldwide ocean color monitoring for the study of biological oceanography KOMPSAT will be launched in the middle of November this year. The radiometric performance of OSMI is analyzed for various gain settings in the viewpoint of the instrument developer for OSMI calibration and application based on its ground performance measurement data for 8 primary spectral bands of OSMI. The radiometric response linearity and dynamic range are analyzed for the image radiometric calibration and the estimation of OSMI image quality for the ocean remote sensing area. The dynamic range is compared with the nominal input radiance for the ocean and the land. The noise equivalent radiance (NER) corresponding to the instrument radiometric noise is compared with the radiometric resolution of signal digitization (1-count equivalent radiance). The best gain setting of OSMI for ocean monitoring is recommended. This analysis is considered to be useful for the OSMI mission and operation planning, the OSMI image data calibration, and users' understanding about OSMI image quality.

• Proceedings of the Optical Society of Korea Conference
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• 2005.02a
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• pp.44-45
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• 2005

• Korean Journal of Remote Sensing
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• v.24 no.2
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• pp.213-222
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• 2008

Although hyperspectral sensing data have shown great potential to derive various surface information that is not usually available from conventional multispectral image, the acquisition of proper hyperspectral image data are often limited. To use ground-based hyperspectral camera image for remote sensing studies, radiometric calibration should be prerequisite. The objective of this study is to develop radiometric calibration procedure to convert image digital number (DN) value to surface reflectance for the 120 bands ground-based hyperspectral camera. Hyperspectral image and spectral measurements were simultaneously obtained from the experimental target that includes 22 different surface materials of diverse spectral characteristics at wavelength range between 400 to 900 nm. Calibration coefficients to convert image DN value to at-sensor radiance were initially derived from the regression equations between the sample image and spectral measurements using ASD spectroradiometer. Assuming that there is no atmospheric effects when the image acquisition and spectral measurements were made at very close distance in ground, we were also able to derive calibration coefficients that directly transform DN value to surface reflectance. However, these coefficients for deriving reflectance values should not be applied when the camera is used for aerial image that contains significant effect from atmosphere and further atmospheric correction procedure is required in such case.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.203-215
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• 2019

As hyperspectral sensors that can be mounted on drones are developed, it is possible to acquire hyperspectral imagery with high spatial and spectral resolution. Although the importance of atmospheric correction has been reduced since imagery of drones were acquired at a low altitude,studies on the conversion process from raw data to spectral reflectance should be done for studies such as estimating the concentration of surface materials using hyperspectral imagery. In this study, a vicarious radiometric calibration and an atmospheric correction algorithm based on atmospheric radiation transfer model were applied to hyperspectral data of drone and the results were compared and analyzed. The vicarious calibration method was applied to an empirical line calibration using the spectral reflectance of a tarp made of uniform material. The atmospheric correction algorithm used ATCOR-4 based Modran-5 that was widely used for the atmospheric correction of aerial hyperspectral imagery. As a result of analyzing the RMSE of the difference between the reference reflectance and the correction, the vicarious calibration using the tarp in a single period of hyperspectral image was the most accurate, but the atmospheric correction was possible according to the application purpose of using hyperspectral imagery. If the correction process of normalized spectral reflectance is carried out through the additional vicarious calibration for imagery from multiple periods in the future, accurate analysis using hyperspectral drone imagery will be possible.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.53-56
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• 2007

The Korean Geostationary satellite (COMS) to fly in year 2009 will carry a meteorological sensor from which visible channel measurements will be available. We developed a method utilizing satellite-derived BRDFs for the solar channel calibration over the bright desert area. The 6S model has been incorporated to account for directional effects of the surface using MODIS-derived BRDF parameters within the spectral interval in interest. Simulated radiances over the desert targets were compared with MODIS and SeaWiFS measured spectral radiances in order to examine the feasibility of the developed calibration algorithm. We also simulated TOA radiance over ocean targets to verify the consistency and reliability of the result. It was shown that simulated 16-day averaged radiances are in good agreement with the satellite-measured radiances within about ${\pm}5%$ uncertainty range for the year 2005, suggesting that the developed algorithm can be used for calibrating the COMS visible channel within about 5% uncertainty level.