• Korean Journal of Remote Sensing
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• v.22 no.6
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• pp.565-574
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• 2006

Satellite images have been used to obtain land cover information that is one of important factors for hydrological analysis over a large area. In urban area, more detailed land cover data are often required for hydrological analysis because of the relatively complex land cover types. The number of land cover classes that can be classified with traditional multispectral data is usually less than the ones required by most hydrological uses. In this study, we present the capabilities of hyperspectral data (Hyperion) for the classification of hydrological land cover types in urban area. To obtain 17 classes of urban land cover defined by the USDA SCS, spectral mixture analysis was applied using eight endmembers representing both impervious and pervious surfaces. Fractional values from the spectral mixture analysis were then reclassified into 17 cover types according to the ratio of impervious and pervious materials. The classification accuracy was then assessed by aerial photo interpretation over 10 sample plots.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.12
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• pp.2208-2213
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• 2007

In this study we have developed a hyperspectrum imaging system for highly sensitive and effective imaging analysis. An optical setup was designed using acoustic optical tunable filter (AOTF) for high sensitive hyperspectrum imaging. Light emitted by mercury lamp gets split in to diffracted and undiffracted beams while passing though AOTF. GFP transfected HEK-293 cell line was used as a model for in vitro imaging analysis. Cells were first, analyzed by fluorescence microscope followed by flow cytometric analysis. Flow cytometric analysis showed 66.31% transfection yield in GFP transfected HEK-293 cells. Various images of GFP transfected HEK-293 cell were grabbed by collecting the diffracted light using a CCD over a dynamic range of frequency of 129-171 MHz with an interval of 3 MHz. Subsequently, for in vivo image analysis of GFP transfected cells in mouse, a whole-body-imaging system was constructed. The blue light of 488 nm wavelength was obtained from a Xenon arc lamp using an appropriate filter and transmitted through an optical cable to a ring illuminator. To check the efficacy of the newly developed whole-body-imaging system, a comparative imaging analysis was performed on a normal mouse in presence and absence of Xenon arc irradiation. The developed hyperspectrum imaging analysis with AOTF showed the highest intensity of green fluorescent protein at 153 MHz of frequency and 494 nm of wavelength. However, the fluorescence intensity remained same as that of the background below 138 MHz (475 nm) and above 162 MHz (532 nm). The mouse images captured using the constructed whole-body-imaging system appeared monochromatic in absence of Xenon arc irradiation and blue when irradiated with Xenon arc lamp. Nevertheless, in either case mouse images appeared clearly.

• Conservation Science in Museum
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• v.29
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• pp.1-32
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• 2023

The Paintings of a 60^th Wedding Anniversary Ceremony Created by an Unknown Painter (Deoksu 6375), housed by the National Museum of Korea, is a five-panel painting book depicting scenes from a wedding ceremony. Hoehonrye is a type of repeated wedding ceremony to commemorate a couple's 60th wedding anniversary with congratulations from the community. The paintings of the book record five scenes from the wedding: jeoninrye, a ceremony where the groom brings a wooden wild goose to the bride's house; gyoberye, the groom and the bride bowing to each other; heosurye, pouring liquor to toast to the couple's longevity; jeopbin, offering tea to guests; and a banquet to celebrates the couple's 60th wedding anniversary. The book describes figures, buildings and a variety of items in detail with delicate brushstrokes. The techniques were examined using microscopy, infrared, and X-ray irradiation and hyperspectral imaging analysis. The invisible parts were examined to identify the rough sketch and distinguish pigments and dyes used for each color. The components of the pigments were determined by X-ray fluorescence analysis, while the dyes were identified by UV-vis spectrometry. Microscope observation revealed that the fabric used for the paintings was raw silk thread with almost no fiber twist, and plain silk fabric. Hyperspectral imaging analysis, X-ray fluorescence analysis, and UV-vis spectrometry confirmed that the white pigment was white lead and the black was chinese ink. The red pigments were using red clay, cinnabar, and a mixture of cinnabar and minium. Brown was made using red clay and organic dyes, and yellow using gamboge. Green was identified as indigo, malachite, chrome green, barium sulfide, and blue as azurite, smalt, and indigo. The purple dye was estimated as a mixture of indigo and cochineal, and gold parts were used gold powder. Hyperspectral images were distinguished parts damaged and conservation treatment area.

• Journal of Biosystems Engineering
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• v.40 no.4
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• pp.394-408
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• 2015

Spectroscopy is an emerging technology for the quality assessment of pharmaceutical samples, from tablet manufacturing to final quality assurance. The traditional methods for the quality management of pharmaceutical tablets are time consuming and destructive, while spectroscopic techniques allow rapid analysis in a non-destructive manner. The advantage of spectroscopy is that it collects both spatial and spectral information (called hyperspectral imaging), which is useful for the chemical imaging of pharmaceutical samples. These chemical images provide both qualitative and quantitative information on tablet samples. In the pharmaceutics, spectroscopic techniques are used for a variety of applications, such as analysis of the homogeneity of powder samples as well as determination of particle size, product composition, and the concentration, uniformity, and distribution of the active pharmaceutical ingredient in solid tablets. This review paper presents an introduction to the applications of various spectroscopic techniques such as hyperspectroscopy and vibrational spectroscopies (Raman spectroscopy, FT-NIR, and IR spectroscopy) for the quality and safety assessment of pharmaceutical solid dosage forms. In addition, various chemometric techniques that are highly essential for analyzing the spectroscopic data of pharmaceutical samples are also reviewed.

• Current Optics and Photonics
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• v.6 no.1
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• pp.92-103
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• 2022

The development of midinfrared (mid-IR) quantum cascade lasers (QCLs) has enabled rapid high-contrast measurement of the mid-IR spectra of biological tissues. Several studies have compared the differences between the mid-IR spectra of colon cancer and noncancerous colon tissues. Most mid-IR spectrum classification studies have been proposed as machine-learning-based algorithms, but this results in deviations depending on the initial data and threshold values. We aim to develop a process for classifying colon cancer and noncancerous colon tissues through a deep-learning-based convolutional-neural-network (CNN) model. First, we image the midinfrared spectrum for the CNN model, an image-based deep-learning (DL) algorithm. Then, it is trained with the CNN algorithm and the classification ratio is evaluated using the test data. When the tissue microarray (TMA) and routine pathological slide are tested, the ML-based support-vector-machine (SVM) model produces biased results, whereas we confirm that the CNN model classifies colon cancer and noncancerous colon tissues. These results demonstrate that the CNN model using midinfrared-spectrum images is effective at classifying colon cancer tissue and noncancerous colon tissue, and not only submillimeter-sized TMA but also routine colon cancer tissue samples a few tens of millimeters in size.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.89-89
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• 2021

부영양화된 호수나 유속이 느린 하천에서 발생하는 녹조의 과도한 발생은 하천 생태계 훼손, 동식물의 건강, 담수의 오염 등 환경 사회 경제적으로 큰 피해를 준다. 현재 수질 측정망은 정해진 지점에서 Chlorophyll-a(Chl-a), Phycocyanin(PC)을 대표농도로 산정하고 조류경보에 활용하고 있으나, 일주일에 한번씩 샘플링을 통해 Chl-a 및 PC를 측정하여 시공간적인 신뢰성의 문제가 제기될 수 있다. 본 연구에서는 기존 점단위 조류 모니터링의 한계점을 개선하기 위해 초분광영상 자료를 머신러닝 기법에 적용하여 Chl-a 및 PC 산정 알고리즘을 개발하였다. 이를 위해 Chl-a와 PC의 최대 흡수, 반사 파장대, 주요 물 흡수 파장대 자료를 조합하여 9개의 파장비를 구축하였으며, 기존 연구에서 활용한 머신러닝 기법인 Partial Least Square, Random Forest, Gradient Boosting, Support Vector Machine, K-Nearest Neighbor, Artificial Neural Network를 검토하여 최적 모델을 선정하였다. 학습된 머신러닝의 성능을 R², NSE, RMSE 목적함수를 이용해 평가하였으며, 그 결과 ANN이 각각 PC 0.801, 0.755, 11.774 mg/m³, Chl-a 0.733, 0.622, 8.736 mg/m³로 가장 우수한 성능을 보였다. 최적화 된 ANN 모델을 백제보 상류 2016-2017년 항공 초분광영상에 적용하여 시공간에 따른 조류 분포변화를 평가하고자 한다.

• Korean Journal of Remote Sensing
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• v.34 no.6_3
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• pp.1369-1382
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• 2018

As the number of multispectral satellites increases, it is expected that it will be possible to acquire and use images for periodically. However, there is a problem of data discrepancy due to different overpass time, period and spatial resolution. In particular, the difference in band bandwidths became different reflectance even for images taken at the same time and affect uncertainty in the analysis of vegetation activity such as vegetation index. The purpose of this study is to estimate the band adjustment factor according to the difference of bandwidth with other multispectral satellites for the application of KOMPSAT-3 satellite in agriculture field. The Spectral band adjustment factor (SBAF) were calculated using the hyperspectral satellite images acquired in the desert area. As a result of applying SBAF to the main crop area, the vegetation index showed a high agreement rate of relative percentage difference within 3% except for the Hapcheon area where the zenith angle was 25. For the estimation of SBAF, this study used only one set of images, which did not consider season and solar zenith angle of SBAF variation. Therefore, long-term analysis is necessary to solve SBAF uncertainty in the future.

• Korean Journal of Remote Sensing
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• v.33 no.6_2
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• pp.1061-1073
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• 2017

In this study, images were classified using convolutional neural network (CNN) - a deep learning technique - to investigate the feasibility of information production through a combination of artificial intelligence and spatial data. CNN determines kernel attributes based on a classification criterion and extracts information from feature maps to classify each pixel. In this study, a CNN network was constructed to classify materials with similar spectral characteristics and attribute information; this is difficult to achieve by conventional image processing techniques. A Compact Airborne Spectrographic Imager(CASI) and an Airborne Imaging Spectrometer for Application (AISA) were used on the following three study sites to test this method: Site 1, Site 2, and Site 3. Site 1 and Site 2 were agricultural lands covered in various crops,such as potato, onion, and rice. Site 3 included different buildings,such as single and joint residential facilities. Results indicated that the classification of crop species at Site 1 and Site 2 using this method yielded accuracies of 96% and 99%, respectively. At Site 3, the designation of buildings according to their purpose yielded an accuracy of 96%. Using a combination of existing land cover maps and spatial data, we propose a thematic environmental map that provides seasonal crop types and facilitates the creation of a land cover map.

• Korean Journal of Remote Sensing
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• v.32 no.5
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• pp.529-538
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• 2016

This study, multispectral and hyperspectral sensors were utilized to use radiometric cross validation for the purpose of radiometric quality evaluation of a 'KOMPSAT-3'. Images of EO-1 Hyperion and Landsat-8 OLI sensors taken in PICS site were used. 2 sections that have 2 different types of ground coverage respectively were selected as the site of cross validation based on aerial hyperspectral sensor and TOA Reflectance. As a result of comparison between the TOA reflectance figures of KOMPSAT-3, EO-1 Hyperion and CASI-1500, the difference was roughly 4%. It is considered that it satisfies the radiological quality standard when the difference of figure of reflectance in a comparison to the other satellites is found within 5%. The difference in Blue, Green, Red band was approximately 3% as a comparison result of TOA reflectance. However the figure was relatively low in NIR band in a comparison to Landsat-8. It is thought that the relatively low reflectance is because there is a difference of band passes in NIR band of 2 sensors and in a case of KOMPSAT-3 sensor, a section of 940nm, which shows the strong absorption through water vapor, is included in band pass resulting in comparatively low reflectance. To overcome these conditions, more detailed analysis with the application of rescale method as Spectral Bandwidth Adjustment Factor (SBAF) is required.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.1
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• pp.81-88
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• 2014

The researcher of this study applied PCA on aerial hyper-spectral sensor and selectively combined bands which contain high amount of information, creating five types of PCA images. By applying Spectral Angle Mapping-supervised classification technique on each type of image, classification process was carried out and accuracy was evaluated. The test result showed that the amount of information contained in the first band of PCA-transformation image was 76.74% and the second accumulated band contained 98.40%, suggesting that most of information were contained in the first and the second PCA components. Quantitative classification accuracy evaluation of each type of image showed that total accuracy, producer's accuracy and user's accuracy had similar patterns. What drew the researcher's attention was the fact that the first and the second bands of the PCA-transformation image had the highest accuracy according to the classification accuracy although it was believed that more than four bands of PCA-transformation image should be contained in order to secure accuracy when doing the qualitative classification accuracy.