• Journal of Biosystems Engineering
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• 제38권4호
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• pp.318-326
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• 2013

Purpose: We developed a viability evaluation method for cucumber (Cucumis sativus) seed using hyperspectral reflectance imaging. Methods: Reflectance spectra of cucumber seeds in the 400 to 1000 nm range were collected from hyperspectral reflectance images obtained using blue, green, and red LED illumination. A partial least squares-discriminant analysis (PLS-DA) was developed to predict viable and non-viable seeds. Various ranges of spectra induced by four types of LEDs (Blue, Green, Red, and RGB) were investigated to develop the classification models. Results: PLS-DA models for spectra in the 600 to 700 nm range showed 98.5% discrimination accuracy for both viable and non-viable seeds. Using images based on the PLS-DA model, the discrimination accuracy for viable and non-viable seeds was 100% and 99%, respectively Conclusions: Hyperspectral reflectance images made using LED light can be used to select high quality cucumber seeds.

• 지질공학
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• 제31권4호
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• pp.479-492
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• 2021

We conducted a fundamental study on geological and rock detection via drone-based hyperspectral imaging on various types of small rock samples and interpreted the obtained information to compare and classify rocks. Further, we performed hyperspectral imaging on ten rocks, and compared the peak data value and reflectance of rocks. Results showed a difference in the reflectance and data value of the rocks, indicating that the rock colors and minerals vary or the reflectance is different owing to the luster of the surface. Among the rocks, limestone used for hyperspectral imaging is grayish white, inverted rock contains various sizes and colors in the dark red matrix, and granite comprises colorless minerals, such as white, black, gray, and colored minerals, resulting in a difference in reflectance. The reflectance of the visible ray range in ten rocks was 16.00~85.78%, in the near infrared ray range, the average reflectance was 23.94~86.43%, the lowest in basalt and highest in marble in both cases. This is because of the pores in basalt, which caused the difference in reflectance.

• 한국식품저장유통학회지
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• 제30권2호
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• pp.224-234
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• 2023

We analyzed the major quality characteristics of red pepper powders from various regions and predicted these characteristics nondestructively using shortwave infrared hyperspectral imaging (HSI) technology. We conducted partial least squares regression analysis on 70% (n=71) of the acquired hyperspectral data of the red pepper powders to examine the major quality characteristics. R_c² values of ≥0.8 were obtained for the ASTA color value (0.9263) and capsaicinoid content (0.8310). The developed quality prediction model was validated using the remaining 30% (n=35) of the hyperspectral data; the highest accuracy was achieved for the ASTA color value (R_p²=0.8488), and similar validity levels were achieved for the capsaicinoid and moisture contents. To increase the accuracy of the quality prediction model, we conducted spectrum preprocessing using SNV, MSC, SG-1, and SG-2, and the model's accuracy was verified. The results indicated that the accuracy of the model was most significantly improved by the MSC method, and the prediction accuracy for the ASTA color value was the highest for all the spectrum preprocessing methods. Our findings suggest that the quality characteristics of red pepper powders, even powders that do not conform to specific variables such as particle size and moisture content, can be predicted via HSI.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.213-216
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• 2005

A novel hyperspectral imaging spectrometer controlling spatial and spectral resolution individually has been proposed. This imaging spectrometer uses a zoom lens as a telescope and a focusing element. It can change the spatial resolution fixing the spectral resolution or the spectral resolution fixing the spatial resolution. Here, we report the concept of the hyperspectral imaging spectrometer with the novel zooming function and the optical design of a zoom lens as the focusing element. By using lens module and third-order aberration theory, we have presented the initial design of four-group zoom lens with external entrance pupil. And the optimized zoom lens with a focal length of 50 to 150 mm is obtained from the initial design by the optical design software. As a result, the designed zoom lens shows satisfactory performances in wavelength range of 450 to 900 nm as a focusing element in an imaging spectrometer. Furthermore, the collimator lens of the imaging spectrometer is designed through the third-order aberration correction by using an iterative process.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2008년도 International Symposium on Remote Sensing
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• pp.243-246
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• 2008

The Compact Airborne Imaging Spectrometer System (CAISS) was jointly designed and developed as the hyperspectral imaging system by Korea Aerospace Research Institute (KARI) and ELOP inc., Israel. The primary mission of the CAISS is to acquire and provide full contiguous spectral information with high quality spectral and high spatial resolution for advanced applications in the field of remote sensing. The CAISS consists of six physical units; the camera system, the gyro-stabilized mount, the jig, the GPS/INS, the power inverter and distributor, and the operating system. These subsystems shall be tested and verified in the laboratory before the flight. Especially the camera system of the CAISS shall be calibrated and validated with the calibration equipments such as the integrated sphere and spectral lamps. To improve data quality and availability, it is the most important to understand the mechanism of hyperspectral imaging system and the radiometric and spectral characteristics. This paper presents the major characteristics of camera system on the CAISS and summarizes the results of radiometric and spectral experiment during preliminary system verification.

• 농업과학연구
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• 제47권3호
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• pp.645-655
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• 2020

Bacteria are a very common cause of food poisoning. Moreover, bacteria form biofilms to protect themselves from harsh environments. Conventional detection methods for foodborne bacterial pathogens including the plate count method, enzyme-linked immunosorbent assays (ELISA), and polymerase chain reaction (PCR) assays require a lot of time and effort. Hyperspectral imaging has been used for food safety because of its non-destructive and real-time detection capability. This study assessed the feasibility of using hyperspectral imaging and machine learning techniques to detect biofilms formed by Escherichia coli. E. coli was cultured on a high-density polyethylene (HDPE) coupon, which is a main material of food processing facilities. Hyperspectral fluorescence images were acquired from 420 to 730 nm and analyzed by a single wavelength method and machine learning techniques to determine whether an E. coli culture was present. The prediction accuracy of a biofilm by the single wavelength method was 84.69%. The prediction accuracy by the machine learning techniques were 87.49, 91.16, 86.61, and 86.80% for decision tree (DT), k-nearest neighbor (k-NN), linear discriminant analysis (LDA), and partial least squares-discriminant analysis (PLS-DA), respectively. This result shows the possibility of using machine learning techniques, especially the k-NN model, to effectively detect bacterial pathogens and confirm food poisoning through hyperspectral images.

• Current Optics and Photonics
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• 제4권6호
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• pp.530-539
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• 2020

Hyperspectral images feature a relatively narrow band and are easily disturbed by noise. Accurate estimation of the types and parameters of noise in hyperspectral images can provide prior knowledge for subsequent image processing. Existing hyperspectral-noise estimation methods often pay more attention to the use of spectral information while ignoring the spatial information of hyperspectral images. To evaluate the noise in hyperspectral images more accurately, we have proposed a spectral-spatial cooperative noise-evaluation method. First, the feature of spatial information was extracted by Gabor-filter and K-means algorithms. Then, texture edges were extracted by the Otsu threshold algorithm, and homogeneous image blocks were automatically separated. After that, signal and noise values for each pixel in homogeneous blocks were split with a multiple-linear-regression model. By experiments with both simulated and real hyperspectral images, the proposed method was demonstrated to be effective and accurate, and the composition of the hyperspectral image was verified.

• Journal of Biosystems Engineering
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• 제34권1호
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• pp.37-43
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• 2009

Hyperspectral reflectance and fluorescence scattering have been researched recently for measuring fruit post-harvest quality and condition. And they are promising for nondestructive detection of fruit quality. The objective of this research was to develop a model, which measure the quality of apple by using hyperspectral reflectance and fluorescence. A violet laser (408 nm) and a quartz tungsten halogen light were used as light sources for generating laser induced fluorescence and reflectance scattering in apples, respectively. The laser induced fluorescence and reflectance of 'Golden Delicious' apples were measured by using a hyperspectral imaging system. Fruit firmness, soluble solids and acid content were measured using standard destructive methods. Principal component analyses were performed to extract critical information from both hyperspectral reflectance and fluorescence data and this information was then related to fruit quality indexes. The fluorescence models had poorer predictions of the three quality indexes than the reflectance models. However, the prediction models of integrating fluorescence and reflectance performed consistently better than the individual models of either reflectance or fluorescence. The correlation coefficient for fruit firmness, soluble solid content, and tillable acidity from the integrated model was 0.86, 0.75, and 0.66 respectively. Also the standard errors were 6.97 N, 1.05%, and 0.07% respectively.

• 한국수자원학회논문집
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• 제56권4호
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• pp.235-243
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• 2023

하천법 개정 및 수자원의 조사·계획 및 관리에 관한 법률 제정으로 하상변동조사를 정기적으로 실시하는 것이 의무화되었고, 지자체가 계획적으로 수자원을 관리할 수 있도록 제도가 마련되고 있다. 하상 지형은 직접 측량할 수 없기 때문에 수심 측량을 통해 간접적으로 이루어지고 있으며, 레벨측량이나 음향측심기를 활용한 접촉식 방법으로 이루어지고 있다. 접촉식 수심측량법은 자료수집이 제한적이기 때문에 공간해상도가 낮고 연속적인 측량이 불가능하다는 한계가 있어 최근에는 LiDAR나 초분광영상을 이용한 원격탐사를 이용한 수심측정 기술이 개발되고 있다. 개발된 초분광영상을 이용한 수심측정 기술은 접촉식 조사보다 넓은 지역을 조사할 수 있고, 잦은 빈도로 자료취득이 용이한 드론에 경량 초분광센서를 탑재하여 초분광영상을 취득하고, 최적 밴드비 탐색 알고리즘을 적용해 수심분포 산정이 가능하다. 기존의 초분광 원격탐사 기법은 드론의 경로비행으로 획득한 초분광영상을 면단위의 영상으로 정합한 후 특정 물리량에 대한 분석이 수행되었으며, 수심측정의 경우 모래하천을 대상으로 한 연구가 주를 이루었으며, 하상재료에 대한 평가는 이루어지지 않았었다. 본 연구에서는 기존의 초분광영상을 활용한 수심산정 기법을 식생이 있는 하천에 적용하고, 동일지역에서 식생을 제거한 후의 2가지 케이스에 대해서 시공간 초분광영상과 단면초분광영상에 모두 적용하였다. 연구결과, 식생이 없는 경우의 수심산정이 더 높은 정확도를 보였으며, 식생이 있는 경우에는 식생의 높이를 바닥으로 인식한 수심이 산정되었다. 또한, 기존의 단면초분광영상을 이용한 수심산정뿐만 아니라 시공간 초분광영상에서도 수심산정의 높은 정확도를 보여 시공간 초분광영상을 활용한 하상변동(수심변동) 추적의 가능성을 확인하였다.

• 한국측량학회지
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• 제32권spc4_2호
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• pp.421-430
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• 2014

항공영상, 위성영상 및 초분광영상은 농업, 산림, 수계 해안, 지질, 토지피복 지도 작성 등에 널리 이용되고 있지만, 지적분야에서 이들의 활용은 거의 나타나지 않고 있다. 한편 해외에서는 항공 위성영상의 지적도와 중첩이나 지목의 등록 및 갱신과 관련된 연구 사례들이 보고되고 있다. 이에 본 연구에서는 초분광영상을 지적 분야 적용성 검토결과, 기존 지목 오류 조사를 위한 현장 공간정보 취득 수단으로 활용될 수 있을 것으로 판단되며 향후 다목적 지적 구현 시 지적정보와 융합할 수 있는 농업, 토양, 그리고 식생 등의 속성정보 취득에 기여할 것으로 사료된다.