• Asian-Australasian Journal of Animal Sciences
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• v.18 no.7
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• pp.1036-1040
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• 2005

Near-infrared reflectance spectroscopic (NIRS) calibrations were developed for accurate and fast prediction of whole broiler chicken carcass composition. The Feed and Forage Foss systems Model 5000 Reflectance Transport Model 5000 with near-infrared reflectance spectroscopy (NIRS)-WinISI II windows software was used for this purpose. One equation was developed for the prediction of each carcass component. One hundred and fifty freeze dried broiler whole carcass samples were ground in a Cyclotech 1,093 sample mill and analyzed for dry matter, protein, fat, calcium and phosphate. Samples were divided into two sets: a calibration set from which equations were derived and a prediction set used to validate these equations. The chemical analysis values (mean${\pm}$SD) were calculated based on dry matter basis as follows: dry matter: 33.41${\pm}$2.78 (range: 26.41-43.47), protein: 54.04${\pm}$6.63 (range: 36.20-76.09), fat 35.44${\pm}$8.34 (range: 7.50-55.03), calcium 2.55${\pm}$0.65 (range: 0.99-4.41), phosphorus 1.38${\pm}$0.26 (range: 0.60-2.28). One hundred and three samples were used to calibrate the equations and prediction values. The software used was modified to obtain partial least square regression statistics, as it is the most suitable for natural products analysis. The coefficients of determination ($R^2$) and the standard errors of prediction were 0.82 and 1.83 for the dry matter, 0.96 and 1.98 for protein, 0.99 and 1.07 for fat, 0.90 and 0.30 for calcium and 0.91 and 0.11 for phosphorus, respectively. The present study indicated that NIRS can be calibrated to predict the whole broiler carcass chemical composition, including minerals in a rapid, accurate, and cost effective manner. It neither requires skilled operators nor generates hazardous waste. These findings may have practical importance to improve instrumental procedures for quick evaluation of broiler carcass composition.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.563-569
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• 2021

With the launch of Compact Advanced Satellite 500 series of various characteristics and the operation of KOMPSAT-3/3A, uses of high-resolution satellite images have been continuously increased. Especially, in order to provide satellite images in the form of ARD (Analysis Ready Data), various pre-processing such as geometric correction and radiometric correction have been developed. For pre-processing of high spatial satellite imagery, auxiliary information, such as solar zenith, solar azimuth and offnadir angle, should be required. However, most of the high-resolution satellite images provide the solar zenith and nadir angle for the entire image as a single variable. In this paper, the solar zenith and offnadir angle corresponding to each pixel of the image were calculated using RFM (Rational Function Model) and auxiliary information of the image, and the quality of extracted information were evaluated. In particular, for the utilization of pixel-based solar zenith and offnadir angle, pixel-based auxiliary data were applied in calculating the top of atmospheric reflectance, and comparative evaluation with a single constant-based top of atmospheric reflectance was performed. In the experiments using various satellite imagery, the pixel-based solar zenith and offnadir angle information showed a similar tendency to the auxiliary information of satellite sensor, and it was confirmed that the distortion was reduced in the calculated reflectance in the top of atmospheric reflectance.

• Asian-Australasian Journal of Animal Sciences
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• v.18 no.4
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• pp.580-583
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• 2005

We have evaluated the application of spectroscopy using an insertion-type fiber-optic probe and a sensor at wavelengths from 400 to 1,100 nm to the measurement of milk fat content on dairy farms. The internal reflectance ratios of 183 milk samples were determined with a fiber-optic spectrophotometer at 5$^{\circ}C$, 20$^{\circ}C$ and 40$^{\circ}C$. Partial least squares (PLS) regression was used to develop calibration models for the milk fat. The best accuracy of determination was found for an equation that was obtained using smoothed internal reflectance data and three PLS factors at 20$^{\circ}C$. The correlation coefficients between predicted and reference milk fat at 5$^{\circ}C$, 20$^{\circ}C$ and 40$^{\circ}C$ were r=0.753, r=0.796 and r=0.783, respectively. The predictive explained variances ($Q^2$) of the final model, moreover, were more than 0.550 at all temperatures, and the regression coefficients of determination ($R^2$) were more than 0.6 (60%). Our results indicate that milk has different internal reflectance measured in the range of visible and near infrared wavelengths (400 to 1,100 nm), depending on its fat content.

• Journal of Information Processing Systems
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• v.16 no.2
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• pp.435-446
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• 2020

With the ability to learn rules from training data, the machine learning model can classify unknown objects. At the same time, the dimension of hyperspectral data is usually large, which may cause an over-fitting problem. In this research, an identification methodology of tea diseases was proposed based on spectral reflectance and machine learning, including the feature selector based on the decision tree and the tea disease recognizer based on random forest. The proposed identification methodology was evaluated through experiments. The experimental results showed that the recall rate and the F1 score were significantly improved by the proposed methodology in the identification accuracy of tea disease, with average values of 15%, 7%, and 11%, respectively. Therefore, the proposed identification methodology could make relatively better feature selection and learn from high dimensional data so as to achieve the non-destructive and efficient identification of different tea diseases. This research provides a new idea for the feature selection of high dimensional data and the non-destructive identification of crop diseases.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.307-312
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• 1998

The paper first describes the atmospheric correction algorithm for the Ocean Color and Temperature Scanner (OCTS) visible band data used at Earth Observation Center (EOC) of National Space Development Agency of Japan (NASDA). It uses 10 candidate aerosol models including "Asian dust model" introduced in consideration of the unique feature of aerosols over the east Asian waters. Based on the observations at 670 and 865 nm bands where the reflectance of the water body can be discarded, the algorithm selects a pair of aerosol models that accounts best for the observed spectral reflectances to synthesize the aerosol reflectance in other bands. The paper also evaluates the performance of the algorithm by comparing the satellite estimates of water-leaving radiance and chlorophyll-a concentration with selected buoy-and ship-measured data. In comparison with the old CZCS-type atmospheric correction algorithm where the aerosol reflectance is as-sumed to be spectrally independent, the OCTS algorithm records factor 2-3 less error in estimating the normalized water-leaving radiances. In terms of chlorophyll-a concentration estimation, however, the accuracy stays vey similar compared to that of the CZCS-type algorithm. This is considered to be due to the nature of in-water algorithm which relies on spectral ratio of water-leaving radiances.

• Korean Journal of Remote Sensing
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• v.14 no.3
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• pp.262-276
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• 1998

The paper first describes the atmospheric correction algorithm for the Ocean Color and Temperature Scanner (OCTS) visible band data used at Earth Observation Center (EOC) of National Space Development Agenrr of japan (NASDA). It uses 10 candidate aerosol models including "Asian dust model" introduced in consideration of the unique feature of aerosols over the east Asian waters. Based on the observations at 670 and 865 nm bands where the reflectance of the water body can be discarded, the algorithm selects a pair of aerosol models that accounts best for the observed spectral reflectances to synthesize the aerosol reflectance in other bands. The paper also evaluates the performance of the algorithm by comparing the satellite estimates of water-leaving radiance and chlorophyll-a concentration with selected buoy- and ship-measured data. In comparison with the old CZCS-type atmospheric correction algorithm where the aerosol reflectance is assumed to be spectrally independent, the OCTS algorithm records factor 2-3 less error in estimating the normalized water-leaving radiances. In terms of chlorophyll-a concentration estimation, however, the accuracy stays very similar compared to that of the CZCS-type algorithm. This is considered to be due to the nature of in-water algorithm which relies on spectral ratio of water-leaving radiances.

• Aerospace Engineering and Technology
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• v.4 no.1
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• pp.211-220
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• 2005

The purpose of this study is to examine the image-based atmospheric correction models using the data from Landsat Enhanced Thermal Mapper Plus (ETM+) that have quite similar spectral characteristics to the forthcoming KOrea Multi-Purpose SATellite (KOMPSAT)-2 Multi-Spectral Camera (MSC), and the in-situ measured surface reflectance data during satellite overflight. The main advantage of this type of correction is that it does not require in-situ measurements during each satellite overflight. While substantial differences are present between Top-Of-the Atmosphere (TOA) reflectance and in-situmeasurements, the results showed that Case 1 based on COST model gives most accurate results among three cases. The accuracy of Case 2_1 is very close to Case 1 and its values are smaller than in-situ data. No notable features appear between some bands in the Case 3_1 and in-situ data. It is expected from this study that if the current methods are applied to the IKONOS high resolution data, we will be able to develop the suitable atmospheric correction methods for MSC data.

• Korean Journal of Agricultural Science
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• v.43 no.5
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• pp.715-722
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• 2016

Rapid determination of food quality is important in food distribution. In this study, the chemical properties of oats were analyzed using visible-near infrared (VIS-NIR) spectroscopy. The objective of this study was to develop and validate a predictive model of oat quality by VIS-NIR spectroscopy. A total of 200 oat samples were collected from domestic and import markets. Reflectance spectra, moisture, protein, fat, Fe, and K of oat samples were measured. Reflectance spectra were measured in the wavelength range of 400 - 2,500 nm at 2 nm intervals. The reflectance spectrum of an oat sample was measured after sample cell and reflectance plate spectrum measurement. Preprocessing methods such as normalization and $1^{st}$ and $2^{nd}$ derivations were used to minimize the spectroscopic noise. The partial-least-square (PLS) models were developed to predict chemical properties of oats using a commercial software package, Unscrambler. The PLS models showed the possibility to predict moisture, protein, and fat content of oat samples. The coefficient of determination ($R^2$) of moisture, protein, and fat was greater than 0.89. However, it was hard to predict Fe and K concentrations due to their low concentrations in the oat samples. The coefficient of determinations of Fe and K were 0.57 and 0.77, respectively. In future studies, the stability and practicability of these models should be improved by using a high accuracy spectrophotometer and by performing calibrations with a wider range of oat chemicals.

• Journal of Biosystems Engineering
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• v.33 no.4
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• pp.260-268
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• 2008

Optical diffuse reflectance sensing has potential for rapid and reliable on-site estimation of soil properties. For good results, proper calibration to measured soil properties is required. One issue is whether it is necessary to develop calibrations using samples from the specific area or areas (e.g., field, soil series) in which the sensor will be applied, or whether a general "factory" calibration is sufficient. A further question is if specific calibration is required, how many sample points are needed. In this study, these issues were addressed using data from 42 paddy fields representing 14 distinct soil series accounting for 74% of the total Korean paddy field area. Partial least squares (PLS) regression was used to develop calibrations between soil properties and reflectance spectra. Model evaluation was based on coefficient of determination ($R^2$) root mean square error of prediction (RMSEP), and RPD, the ratio of standard deviation to RMSEP. When sample data from a soil series were included in the calibration stage (full information calibration), RPD values of prediction models were increased by 0.03 to 3.32, compared with results from calibration models not including data from the test soil series (calibration without site-specific information). Higher $R^2$ values were also obtained in most cases. Including some samples from the test soil series (hybrid calibration) generally increased RPD rapidly up to a certain number of sample points. A large portion of the potential improvement could be obtained by adding about 8 to 22 points, depending on the soil properties to be estimated, where the numbers were 10 to 18 for pH, 18-22 for EC, and 8 to 22 for total C. These results provide guidance on sampling and calibration requirements for NIR soil property estimation.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.3
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• pp.3-10
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• 2015

All kinds of objects on the ground have inherent spectral reflectance curves, which can be used to classify the ground objects and to detect the target. Remotely sensed data have to be transferred to spectral reflectance for accurate analysis. There are formula methods provided by the institution, mathematical model method and ground-data-based method. In this study, RapidEye satellite image was converted to reflectance data using spectral reflectance of a CASI hyperspectral image by using vicarious radiometric calibration. The results were compared with those of the other calibration methods and ground data. The proposed method was closer to the ground data than ATCOR and New Kurucz 2005 method and equal with ELM method.