• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 1998년도 Proceedings of International Symposium on Remote Sensing
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• pp.376-381
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• 1998

In this study, we was measured the radiance reflectance by using multi-spectral image of low resolution camera(LRC) which will be loaded in the multi-purpose satellite(KOMPSAT) to use the data in analyzing water pollution. Also we investigated the possibility of extraction of water quality factors in rivers and water body by using high resolution remote sensing data such as Airborne MSS. Especially, we tried to extract the environmental factors related with eutrophication, and also tried to develop the process technique and the radiance feature of reflectance related with eutrophication. The results were summarized as follows: First, the spectrum of sun's rays which reaches the surface of the earth was consistent with visible rays bands of 0.4${\mu}{\textrm}{m}$~0.7${\mu}{\textrm}{m}$ and about 50% of total quantity of radiation were there. And at around 0.5${\mu}{\textrm}{m}$ of green spectral band in visible rays bands, the spectrum was highest. Second, as a result of the radiance reflectance Chlorophyll-a represented high spectral reflectance mainly around 0.52${\mu}{\textrm}{m}$ of green spectral band, and suspended sediments and turbidity represented high spectral reflectance at 0.8${\mu}{\textrm}{m}$ and at 0.57${\mu}{\textrm}{m}$ each. Third, as a result of the water quality analysis by using Airborne MSS, Chlorophyll-a could have a distribution chart when carried out ratio of B3 and BS to B7. And Band 7 was useful for making the distribution chart of suspended sediments. And when we carried out PCA, suspended sediments and turbidity had distributions at PC 1 , PC 4 each similarly to ground truth data. Above results can be changed according to the change of season and time. Therefore, in order to analyze more exactly the environmental factors of water quality by using LRC data, we need to investigate constantly the ground truth data and the radiance feature of reflectance of water body. Afterward in this study, we will constantly analyze the radiance feature of the surface of water in water body by measuring the on-the-spot radiance reflectance and using low resolution satellite image(SeaWiFs). Besides, we will gather the data of water quality analysis in water body and analyze the pattern of water pollution.

• 대한원격탐사학회지
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• 제14권4호
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• pp.343-352
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• 1998

주요 밭작물인 옥수수, 땅콩, 콩에 대한 시계열 분광반사율 자료를 수집하여, 작물의 재배력과 분광반사 특성 차이에 따른 작물간 구분여부를 살펴보았다. 분광반사계를 이용하여 각 작물의 생육기간 동안 0.33~1.10 $mu{m}$ 범위의 파장영역에서 약 12회 분광반사율을 측정하였다. OSMI의 중심 파장역 또는 중심 파장역과 가장 가까운 채널인 445nm, 490nm, 510nm, 555nm, 690nm, 865nm를 각각 OSMI 동등 밴드 1,2,3,4,5,6 으로 하였다. OSMI 동등 밴드와 밴드간 비율을 이용하여 일반선형모형(GLM) 분석한 결과, 분광반사율 측정일 중 옥수수와 두류작물을 구분하기에 가장 적합한 시기는 6월 22일인 것으로 나타났다. 또한 옥수수 생육에 따른 분광반사율과 밴드간 비율을 이용하여 청예용 옥수수인 rs510의 영양생장기 동안 생육단계를 추정하는 다중회귀식을 만들었다.

• 대한원격탐사학회지
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• 제22권3호
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• pp.175-182
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• 2006

Because of the phenological variation of vegetation growth in temperate region, it is often difficult to accurately assess the surface conditions of agricultural croplands, grasslands, and disturbed forests by multi-spectral remote sensor data. In particular, the spectral similarity between soil and dry vegetation has been a primary problem to correctly appraise the surface conditions during the non-growing seasons in temperature region. This study analyzes the spectral characteristics of the mixture of dry vegetation and soil. The reflectance spectra were obtained from laboratory spectroradiometer measurement (GER-2600) and from EO-1 Hyperion image data. The reflectance spectra of several samples having different level of dry vegetation fractions show similar pattern from both lab measurement and hyperspectral image. Red-edge near 700nm and shortwave IR near 2,200nm are more sensitive to the fraction of dry vegetation. The use of hyperspectral data would allow us for better separation between bare soils and other surfaces covered by dry vegetation during the leaf-off season.

• 농업과학연구
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• 제41권3호
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• pp.169-175
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• 2014

Objective of this study was to investigate effects of pre-processing method and number of sampling leaves on stability of the reflectance measurement for Chinese cabbage and kale leaves. Chinese cabbage and kale were transplanted and cultivated in a plant factory. Leaf samples of the kale and cabbage were collected at 4 weeks after transplanting of the seedlings. Spectra data were collected with an UV/VIS/NIR spectrometer in the wavelength region from 190 to 1130 nm. All leaves (mature and young leaves) were measured on 9 and 12 points in the blade part in the upper area for kale and cabbage leaves, respectively. To reduce the spectral noise, the raw spectral data were preprocessed by different methods: i) moving average, ii) Savitzky-Golay filter, iii) local regression using weighted linear least squares and a $1^{st}$ degree polynomial model (lowess), iv) local regression using weighted linear least squares and a $2^{nd}$ degree polynomial model (loess), v) a robust version of 'lowess', vi) a robust version of 'loess', with 7, 11, 15 smoothing points. Effects of number of sampling leaves were investigated by reflectance difference (RD) and cross-correlation (CC) methods. Results indicated that the contribution of the spectral data collected at 4 sampling leaves were good for both of the crops for reflectance measurement that does not change stability of measurement much. Furthermore, moving average method with 11 smoothing points was believed to provide reliable pre-processed data for further analysis.

• 한국지반신소재학회논문집
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• 제20권4호
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• pp.141-149
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• 2021

본 연구에서는 초분광정보를 이용한 지반의 함수비 예측 기술을 위해 실내 분광정보 실험을 수행하여 세립분 및 함수비 조건에 따라 특정 파장대역에 따른 분광반사율을 획득하였다. 이를 토대로 기존 문헌의 스펙트럼 지수에 따라 분광정보를 정규화하고, 세립분 및 함수비 조건과의 상관관계 및 예측 기술의 신뢰성을 분석하였다. 상관관계 분석 결과 함수비가 증가할수록 분광반사율은 낮아지며 세립분 함유율이 증가할수록 높은 함수비 조건의 분광반사율은 감소율이 크게 발생하였다. 또한 7가지의 스펙트럼 지수 산정방법을 고찰하여 함수비 예측에 대한 신뢰도를 평가하였다. 이 중 DVI가 비교적 높은 예측 신뢰도를 보이며, 민감도 부분에서도 다른 산정 방법에 비해 우수하여 지반의 함수비 예측 기술 적용에 있어 적합한 스펙트럼 지수인 것으로 판단하였다.

• Journal of Biosystems Engineering
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• 제15권1호
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• pp.33-43
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• 1990

Spectral reflectance of paddy, brown rice, milled rice, greenish grain, yellow grain and denatured grain were measured over the range of 340 to 820 nm with "Double Beam Spectrophotometer(Model UV-180)". Variation in the reflectance depending on milling degree of milled rice appeared greatest over the range of 420 to 500 nm, and that between white rice and other rice samples appeared greatest near 500 nm. On the basis of the above results, a whiteness meter to measure milling degree of rice was manufactured using tungsten lamp, photodiodes and amplifier, and its performance was compared with the existing whiteness meters (KETT and Z-II optical sensor). There were very high correlations among those whiteness meters.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
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• pp.196-200
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• 2004

The purpose of this study is to examine the image­based 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-situ measurements, the results showed that Case 1 based on COST model gives most accurate results among three cases. The accuracy of Case 2 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 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.

• 한국광학회지
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• 제9권5호
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• pp.300-306
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• 1998

광학용 유전체 다층박막의 증착시 높은 열로 인해 형성되는 층간확산의 광학적 효과를 연구하였다. 연속적으로 변하는 굴절률 분포함수를 갖고 하나의 부분구조가 주기적으로 반복되는, 다층박막의 광특성 계산방법을 제시한다. 이 방법을 이용하여 Fabry-Perot형 여과막, 넓은 영역에 걸쳐 높은 반사율을 갖는 전반사경 및 반사 방지막에 대해 층간확산이 미치는 영향을 계산하여 투과 및 반사 스펙트럼을 얻었다. 층간확산으로 인하여 전반사경의스펙트럼 대역폭이 줄어들었으며 다층 반사방지막의 경우층의 수가 많을 때 특성이 악화되고 파장에 따른 반사율의 변화가 크게 되었다.

• Journal of Biosystems Engineering
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• 제22권2호
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• pp.227-236
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• 1997

Manual grading and sorting of red-ginsengs are inherently unreliable due to its subjective nature. A computerized technique based on optical and geometrical characteristics was studied for the objective quality evalution. Spectral reflectance of three categories of red-ginsengs - "Chunsam", "Chisam", "Yangsam" - were measured and analyzed. Variation of reflectance among parts of a single ginseng was more significant than variation among the quality categories of ginsengs. A PC-based image processing algorithm was developed to extract geometrical features such as length and thickness of body, length and number of roots, position of head and branch point, etc. The algorithm consisted of image segmentation, calculation of Euclidean distance, skeletonization and feature extraction. Performance of the algorithm was evaluated using sample ginseng images and found to be mostly sussessful.

• Journal of Information Processing Systems
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• 제16권2호
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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.