• 대한원격탐사학회지
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• 제37권3호
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• pp.449-461
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• 2021

광학 위성정보에 대한 분석대기자료(ARD)는 각 센서 별 분광특성과 촬영각 등을 적용하는 전처리 작업에 의한 성과물이다. 대기보정 처리과정은 통하여 얻을 수 있는 대기반사도와 지표반사도는 기본적이면서 복잡한 알고리즘을 요구한다. 대부분 위성 정보처리 소프트웨어에서는 Landsat 위성 대기보정 처리 알고리즘 및 기능을 제공하고 있다. 또한 사용자는 클라우드 환경에서 Google Earth Engine(GEE)을 통하여 USGS-ARD와 같은 Landsat 반사도 성과에 직접 접근할 수 있다. 이번 연구에서는 고해상도 위성정보 처리에 활용되고 있는 Orfeo ToolBox(OTB) 오픈 소스 소프트웨어의 대기보정 기능을 확장 구현하였다. 현재 OTB 도구는 어떠한 Landsat 센서도 지원하지 않기 때문에, 이 확장 도구는 최초로 개발된 사례이다. 이 도구를 이용하여 RadCalNet 사이트의 Railroad Valley, United States(RVUS) 반사율 자료 값을 이용한 결과 검증을 위하여 같은 지역의 Landsat-8 OLI 영상의 절대 대기보정에 의한 반사도 성과를 산출하였다. 산출된 결과는 RVUS 자료를 기준으로 반사도 값과의 차이가 5% 미만으로 나타났다. 한편 이 반사도 성과는 USGS-ARD 반사도 값뿐만 아니라 QGIS Semi-automatic Classification Plugin과 SAGA GIS와 같은 다른 오픈 소스 도구에서 산출된 성과를 이용한 비교 분석을 수행하였다. OTB 확장도구로부터 산출한 반사도 성과는 RadCalNet RVUS의 자료와 높은 일치도를 나타내는 USGS-ARD의 값과 가장 부합되는 것으로 나타났다. 이 연구에서 OTB 대기보정 처리의 다양한 위성센서 적용 가능성을 입증한 결과로 이 모듈을 다른 센서정보로 확장하여 구현하는 경우에도 정확도가 높은 반사도 산출이 가능한 것을 확인할 수 있었다. 이와 같은 연구 방법은 향후 차세대중형위성을 포함하는 다양한 광학위성에 대한 반사도 성과 산출 도구개발에도 활용할 수 있다.

• 한국물환경학회지
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• 제35권6호
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• pp.589-597
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• 2019

The purpose of this study was improve accuracy the IOPs inversion model(IOPs-IM) developed in 2016 for phycocyanin(PC) concentration estimation in the Nakdong River. Additionally, two optimum models were developed and evaluated with 2017 measurement field spectral data for the Geum River and the Yeongsan River. The used measurement data for IOPs-IM analyzation was randomly classified as training and verification materials at the ratio of 2:1 in all data sets. Using the training data set from 2015-2017, accuracy results of the IOPs-IM generally improved for the Nakdong River. The RMSE(Root Mean Square Error) decreased by 14 % compared to 2016. For the GeumRiver, the results of the IOPs-IM were suitable, except for some point results in 2016. Results of the IOPs-IM in the Yeongsan River followed the overall 1:1 line and MAE(Mean Absolute Error) was lower than other rivers. But the RMSE and MAE values were higher. As a result of applying the validation data to the IOPs-IM, the accuracy of the Nakdong River was reduced to RMSE 17.7 % and MRE 16.4 %, respectively compared with 2016. However, the MRE(Mean Relative Error) was estimated to be higher by 400 % in the Geum River, and the RMSE was more than 100 mg/㎥ of the Yeongsan River. Therefore, it is necessary to get the continuously data with various sections of each river for obtain objective and reliable results and the models should be improved.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1131-1131
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• 2001

To develop a nondestructive quality evaluation technique of fruits, a K-mean algorism is applied to near infrared (NIR) spectroscopy of apples. The K-mean algorism is one of neural network partition methods and the goal is to partition the set of objects O into K disjoint clusters, where K is assumed to be known a priori. The algorism introduced by Macqueen draws an initial partition of the objects at random. It then computes the cluster centroids, assigns objects to the closest of them and iterates until a local minimum is obtained. The advantage of using neural network is that the spectra at the wavelengths having absorptions against chemical bonds including C-H and O-H types can be selected directly as input data. In conventional multiple regression approaches, the first wavelength is selected manually around the absorbance wavelengths as showing a high correlation coefficient between the NIR $2^{nd}$ derivative spectrum and Brix value with a single regression. After that, the second and following wavelengths are selected statistically as the calibration equation shows a high correlation. Therefore, the second and following wavelengths are selected not in a NIR spectroscopic way but in a statistical way. In this research, the spectra at the six wavelengths including 900, 904, 914, 990, 1000 and 1016nm are selected as input data for K-mean analysis. 904nm is selected because the wavelength shows the highest correlation coefficients and is regarded as the absorbance wavelength. The others are selected because they show relatively high correlation coefficients and are revealed as the absorbance wavelengths against the chemical structures by B. G. Osborne. The experiment was performed with two phases. In first phase, a reflectance was acquired using fiber optics. The reflectance was calculated by comparing near infrared energy reflected from a Teflon sphere as a standard reference, and the $2^{nd}$ derivative spectra were used for K-mean analysis. Samples are intact 67 apples which are called Fuji and cultivated in Aomori prefecture in Japan. In second phase, the Brix values were measured with a commercially available refractometer in order to estimate the result of K-mean approach. The result shows a partition of the spectral data sets of 67 samples into eight clusters, and the apples are classified into samples having high Brix value and low Brix value. Consequently, the K-mean analysis realized the classification of apples on the basis of the Brix values.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1032-1032
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• 2001

Artificial Neural Network (ANN) calibration techniques have been used commercially for agricultural applications since the mid-nineties. Global models, based on transmission data from 850 to 1050 nm, are used routinely to measure protein and moisture in wheat and barley and also moisture in triticale, rye, and oats. These models are currently used commercially in approx. 15 countries throughout the world. Results concerning earlier European ANN models are being published elsewhere. Some of the findings from that study will be discussed here. ANN models have also been developed for coarsely ground samples of compound feed and feed ingredients, again measured in transmission mode from 850 to 1050 nm. The performance of models for pig- and poultry feed will be discussed briefly. These models were developed from a very large data set (more than 20,000 records), and cover a very broad range of finished products. The prediction curves are linear over the entire range for protein, fat moisture, fibre, and starch (measured only on poultry feed), and accuracy is in line with the performance of smaller models based on Partial Least Squares (PLS). A simple bias adjustment is sufficient for calibration transfer across instruments. Recently, we have investigated the possible use of ANN for a different type of NIR spectrometer, based on reflectance data from 1100 to 2500 nm. In one study, based on data for protein, fat, and moisture measured on unground compound feed samples, dedicated ANN models for specific product classes (cattle feed, pig feed, broiler feed, and layers feed) gave moderately better Standard Errors of Prediction (SEP) compared to modified PLS (MPLS). However, if the four product classes were combined into one general calibration model, the performance of the ANN model deteriorated only slightly compared to the class-specific models, while the SEP values for the MPLS predictions doubled. Brix value in molasses is a measure of sugar content. Even with a huge dataset, PLS models were not sufficiently accurate for commercial use. In contrast an ANN model based on the same data improved the accuracy considerably and straightened out non-linearity in the prediction plot. The work of Mr. David Funk (GIPSA, U. S. Department of Agriculture) who has studied the influence of various types of spectral distortions on ANN- and PLS models, thereby providing comparative information on the robustness of these models towards instrument differences, will be discussed. This study was based on data from different classes of North American wheat measured in transmission from 850 to 1050 nm. The distortions studied included the effect of absorbance offset pathlength variation, presence of stray light bandwidth, and wavelength stretch and offset (either individually or combined). It was shown that a global ANN model was much less sensitive to most perturbations than class-specific GIPSA PLS calibrations. It is concluded that ANN models based on large data sets offer substantial advantages over PLS models with respect to accuracy, range of materials that can be handled by a single calibration, stability, transferability, and sensitivity to perturbations.

• 한국대기환경학회지
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• 제17권5호
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• pp.395-405
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• 2001

To make an assessment of the compatibility between DOAS and conventional point monitoring system (MCSAM-2: MS2), we investigated the concentrations of three criteria pollutants which include S $O_2$, N $O_2$, and $O_3$from a national monitoring station in Seoul during the periods of June 1999~August 2000. The average concentration values for the whole study period derived from hourly concentration data sets of those three species indicated that the mean differences between the two methods can be approximated as 18%. When the bias structure of two systems was evaluated through the computation of percent difference(PD) between the two such as ( $C_{DOAS}$- $C_{conventional}$ $C_{DOAS}$*100, differences between the two systems appeared to be quite systematic among different compounds. While the mode of bias peaked at 0~20% or 20~40% in terms of PD values, the cause of such positive bias mainly arised from generally enhanced concentration values of DOAS system. The structure of bias among different species was further assessed through linear regression analysis. Results of the analysis indicated that the dominant portions of differences observed from two monitoring systems can be accounted for by the systematic differences in their spanning and zeroing systems. S $O_2$(MS2)=0.6385 S $O_2$(DOAS)+2.0985($r^2$=0.7894) N $O_2$(MS2)=0.6548 N $O_2$(DOAS)+7.437($r^2$=0.7687) $O_3$(MS2)=1.0359 $O_3$(DOAS)-7.7885($r^2$=0.7944) The findings of slope values at around 0.64~0.65 from two species suggest that DOAS should respond more sensitively in upper bound concentration range. The offset values apart from zero indicate that more deliberate comparison needs to be made between these monitoring systems. However, based on the existence of strong correlations from at least 8,000 data points for each species of comparison, we were able to conclude that the compatibility of two monitoring systems is highly significant. With the improvement of calibration techniques for the DOAS system. its applicability for routine monitoring of airborne pollutant species is expected to be quite extendable.

• 천문학회지
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• 제49권1호
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• pp.25-35
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• 2016

Multiple color selection techniques are successful in identifying quasars from wide-field broadband imaging survey data. Among the quasars that have been discovered so far, however, there is a redshift gap at 5 ≲ z ≲ 5.7 due to the limitations of filter sets in previous studies. In this work, we present a new selection technique of high redshift quasars using a sequence of medium-band filters: nine filters with central wavelengths from 625 to 1025 nm and bandwidths of 50 nm. Photometry with these medium-bands traces the spectral energy distribution (SED) of a source, similar to spectroscopy with resolution R ~ 15. By conducting medium-band observations of high redshift quasars at 4.7 ≤ z ≤ 6.0 and brown dwarfs (the main contaminants in high redshift quasar selection) using the SED camera for QUasars in EArly uNiverse (SQUEAN) on the 2.1-m telescope at the McDonald Observatory, we show that these medium-band filters are superior to multi-color broad-band color section in separating high redshift quasars from brown dwarfs. In addition, we show that redshifts of high redshift quasars can be determined to an accuracy of Δz/(1 + z) = 0.002 - 0.026. The selection technique can be extended to z ~ 7, suggesting that the medium-band observation can be powerful in identifying quasars even at the re-ionization epoch.

• 한국지리정보학회지
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• 제18권3호
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• pp.76-88
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• 2015

이 논문에서는 작물 재배지의 분류를 목적으로 능동 학습과 과거 토지 피복도 기반의 시간 문맥 정보를 결합하는 분류 방법론을 제안하였다. 신뢰성 높은 훈련 자료의 추출을 위하여 능동 학습 기반 반복 분류를 적용하였으며, 과거 토지 피복도의 작물 재배 규칙을 시간 문맥 정보로 정량화 하여 능동 학습 기법의 적용시 훈련 자료의 할당과 작물 간 분광학적 혼재 효과 완화에 이용하였다. 제안 분류 방법론의 적용 가능성을 평가하기 위해 미국 Illinois 주의 옥수수와 콩 재배지역의 구분을 목적으로 MODIS 시계열 식생지수 자료와 과거 cropland data layer(CDL) 자료를 이용한 사례연구를 수행하였다. 사례연구 결과, 초기 감독 분류 결과에서 나타났던 옥수수와 콩의 오분류와 기타 작물과 비작물의 오분류 양상이 능동 학습 기반 반복 분류를 통해 완화되었다. 그리고 CDL 자료로부터 추출한 시간 문맥 정보를 추가적으로 결합함으로써 주요 작물에서 나타나는 과추정 양상이 완화되어 가장 우수한 분류 정확도를 나타내었다. 따라서 제안 기법이 양질의 훈련 자료의 확보가 쉽지 않은 작물 재배지의 분류에 유용하게 적용될 수 있음을 확인하였다.

• 대한화학회지
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• 제53권6호
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• pp.717-726
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• 2009

Triton X-100이 함유된 상태에서 정색시약인 1-(2-thiazolylazo)-2-naphthol이 첨가된 물에서 구리 (II), 니켈(II)과 아연(II)의 동시 분광광도법적 정량을 위한 다변량 모델들이 개발되었다. 분광학적 간섭의 단점을 극복하기 위해서, 주성분회귀분석법(PCR)과 부분최소자승법(PLS) 다변량 분석법적 접근이 적용되었다. 다양한 시험 세트를 사용하여 본 방법의 수행이 입증되었고 그 결과들이 비교되었다. 일반적으로 PLS와 PCR 모델들 사이에 분석적 수행에서의 심각한 차이가 없었다. $Cu^{2+}$, $Ni^{2+}$ and $Zn^{2+}$ 의 세 성분들을 사용한 예측의 제곱근 평균 제곱 오차(RMSEP)들은 각각 0.018, 0.010, 0.011 ppm이었다. 또한 감도, 분석감도, 검출한계(LOD)와 같은 가치들의 측면들이 평가되었다. 본 논문에서 제안하는 과정이 화합물 혼합용액과 수돗물 속의 $Cu^{2+}$, $Ni^{2+}$ and $Zn^{2+}$의 동시 검출에 적용되었을 때에 높은 신뢰도가 성취되었다.

• 농업과학연구
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• 제48권2호
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• pp.299-310
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• 2021

Glycerol is a non-volatile compound with no aromatic properties that contributes significantly to the quality of wine by providing sweetness and richness of taste. In addition, it is also the third most significant byproduct of alcoholic fermentation in terms of quantity after ethanol and carbon dioxide. In this study, Fourier transform infrared (FT-IR) spectroscopy was employed as a fast non-destructive method in conjugation with multivariate regression analysis to build a model for the quantitative analysis of glycerol concentration in wine samples. The samples were prepared by using three varieties of red wine samples (i.e., Shiraz, Merlot, and Barbaresco) that were adulterated with glycerol in concentration ranges from 0.1 to 15% (v·v^-1), and subjected to analysis together with pure wine samples. A net analyte signal (NAS)-based methodology, called hybrid linear analysis in the literature (HLA/GO), was applied for predicting glycerol concentrations in the collected FT-IR spectral data. Calibration and validation sets were designed to evaluate the performance of the multivariate method. The obtained results exhibited a high coefficient of determination (R²) of 0.987 and a low root mean square error (RMSE) of 0.563% for the calibration set, and a R² of 0.984 and a RMSE of 0.626% for the validation set. Further, the model was validated in terms of sensitivity, selectivity, and limits of detection and quantification, and the results confirmed that this model can be used in most applications, as well as for quality assurance.

• 대한원격탐사학회지
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• 제26권2호
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• pp.251-262
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• 2010

GOCI(Geostationary Ocean Color Imager) 표준자료의 지속적인 품질관리를 위해서는 위성 운용기간 중 궤도상 복사보정, 대기보정 단계를 거쳐야 되며 해수환경 분석 알고리즘에 대한 검보정도 지속적으로 이루어져야 한다. GOCI의 복사, 대기, 해양환경 자료에 대한 검보정은 부이나 고정 플랫폼을 이용한 수온, 염분, 해수 광특성, 형광, 및 탁도 관측과, 주기적으로 해양환경 자료 수집을 통하여 실시한다. 이를 위하여 동중국해에 위치하고 있는 이어도 종합해양과학기지에 설치된 광학 관측 장비와 현장 관측의 복사자료를 상호 비교해 보았으며, GOCI 표준자료의 검정에 앞서 SeaWiFS 복사량과 비교하여 검정하였다. 해수출 광량은 현장관측에서 얻어진 광과 광량과는 약간의 차이를 보였지만, 흡광영역이 매우 잘 일치하고 있으며 스펙트럴 이동은 없는 것으로 판단된다. 이어도 종합해양과학기지의 분광측정기와 SeaWiFS의 전 밴드에서 얻어진 해수출 광량을 비교한 결과 평균 25% 정도의 에러가 발생했지만, 대기보정 밴드를 제외하면 절대오차가 11% 정도로 상당히 낮아진다. 이것은 SeaWiFS 표준 대기보정 방법의 문제점으로 GOCI 검보정 연구에서 고려되어 보완 되어야 할 것으로 판단된다. 이와 더불어 독도 지역의 표준 관측치(Reference Target Site) 구축을 통한 검보정 연구를 위하여, 독도 주변 해수의 광 특성과 해양환경 자료는 2009년 8월과 2009년 10월 2차례에 걸쳐서 현장관측을 실시하였다. 독도 주변 해역의 해양 광 특성은 원격반사도의 스펙트럼형태를 기준으로 Case-1 Water 성향이 강한 해수에서 나타나는 특성과 매우 유사하였다. 식물플랑크톤, 부유물질, 용존유기물의 흡광계수 스펙트럼의 형태들은 대체적으로 각 성분별 흡광 스펙트럼 특성을 잘 보여주었다. 또한 MODIS Aqua로부터 산출된 엽록소 농도와 현장관측을 통한 검증에서 위성자료 값들은 잘 일치한다. 위와 같이 현재 진행되고 있는 GOCI 검보정 연구를 통해서 복사, 대기, 해양환경 알고리즘에 대한 문제점이 도출되었고, 차후 검보정 계획에 반영하여 이 부분들에 대한 개선 및 보완이 이루어질 것으로 판단된다.