• Journal of Environmental Science International
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• v.33 no.7
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• pp.533-546
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• 2024

The coastal urchin barren phenomenon in our country began to spread and expand from the 1980s, centering on the southern coast and Jeju Island, and by the 1990s, it appeared along the east coast and nationwide. The urchin barren phenomenon is mainly conducted through field surveys by diving, but recently, various surveying techniques have been applied. In this study, a spectral library for terrestrial and marine areas was established for the identification of urchin barrens using airborne hyperspectral imagery, and the distribution area was analyzed through the SAM (spectral angle mapper) algorithm. An analysis of the urchin barren phenomenon in the five islands of the West Sea revealed that it occurrs in most areas, with the combined severity of the urchin barren phenomenon in Sapsido and Oeyeondo being approximately 19.9%. Hyperspectral imagery is expected to be highly useful not only for detecting the urchin barren phenomenon but also for managing and monitoring marine fishery resources through the classification of seaweeds.

• Korean Journal of Optics and Photonics
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• v.32 no.5
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• pp.215-219
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• 2021

Recently a system for nondestructive measurement of seedling plants in real time has been attracting attention as an essential element in fields such as the "smart farm". This study reports the simultaneous measurement of anthocyanin accumulations in leaf tissues in a large number of bok choy, using a hyperspectral imaging system. To measure many seedlings simultaneously, an existing hyperspectral imaging system is modified. In this paper, a total of 96 seedlings are measured: 24 each of 4 cultivars. Using the hyperspectral data-acquisition system, 12 seedlings can be analyzed simultaneously within 3 minutes. The hyperspectral imaging technology proposed in this paper is shown to provide an analytic system comparable to destructive chemical analysis. This hyperspectral imaging technology can be applied to a high-throughput plant-phenotyping system, owing to its capability of measuring a large number of specimens at the same time.

• Korean Journal of Remote Sensing
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• v.27 no.5
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• pp.565-572
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• 2011

A modified iterative N-FINDR algorithm is developed for fully automatic extraction of endmembers from hyperspectral image data. This algorithm exploits the advantages of iterative NFINDR technique and Iterative Error analysis technique. The experiments using a simulated hyperspectral image data shows that the optimum number of endmembers can be automatically decided. The extracted endmembers and finally generated abundance fraction maps show the potentialities of the proposed algorithm. More studies are needed for verification of the applicability of the algorithm to the real hyperspectral image data where the absence of pure pixels is common.

• Journal of Biosystems Engineering
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• v.38 no.4
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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.

• The Journal of Engineering Geology
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• v.31 no.4
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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.

• Journal of fish pathology
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• v.37 no.1
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• pp.89-96
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• 2024

Kudoa septempunctata, a myxozoan parasite, usually presents without any signs and primarily infects adult fish. The invasive diagnostic methods, such as tissue biopsy, can identify pathogens, but cause economic losses because they require killing the fish. In this study, we conducted a monitoring of four fish farms located on Jeju Island, to investigate the potential for non-invasive diagnosis of K. septempunctata using hyperspectral cameras. It provides spectral information from R000_B000_G000 to R255_B255_G255 for a total of 3,282 olive flounder (Paralichthys olivaceus). Each object is imaged with 2,000 data points, allowing comprehensive spectral analysis by comparing images obtained from negative control objects to positive control objects. Noticeable differences were observed in the brightness or pallor of the positive control images. This suggests the potential utility of hyperspectral imaging as a non-invasive diagnostic tool for detecting K. septempunctata infections in fish populations.

• Food Science and Preservation
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• v.30 no.2
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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.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1397-1399
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• 2003

When and Where, What kind of agricultural products will be produced and provided for the market? It is a commercial requirement, and also an academic questions to remote sensing technology. Crop physiology analysis and growth monitoring are important elements for precision agriculture management. Remote sensing technology supplies us more selections and available spaces in this dynamic change study by producing images of different spatial, spectral and temporal resolutions. Especially, the hyperspectral remote sensing should do play a key role in crop growth investigation at national, regional and global scales. In the past five years, Chinese academy of sciences and Japan NTT-DATA have made great efforts to establish a prototype information service system to dynamically survey the vegetable planting situation in Nagano area of Japan mainly based on remote sensing data. For such concern, a flexible and light-duty flight system and some practical data processing system and some necessary background information should be rationally made together. In addition, some studies are also important, such as quick pre-processing for hyperspectral data, Multi-temporal vegetation index analysis, hyperspectral image classification in support of GIS data, etc. In this paper, several spectral data analysis models and a designed airborne platform are provided and discussed here.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.203-215
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• 2019

As hyperspectral sensors that can be mounted on drones are developed, it is possible to acquire hyperspectral imagery with high spatial and spectral resolution. Although the importance of atmospheric correction has been reduced since imagery of drones were acquired at a low altitude,studies on the conversion process from raw data to spectral reflectance should be done for studies such as estimating the concentration of surface materials using hyperspectral imagery. In this study, a vicarious radiometric calibration and an atmospheric correction algorithm based on atmospheric radiation transfer model were applied to hyperspectral data of drone and the results were compared and analyzed. The vicarious calibration method was applied to an empirical line calibration using the spectral reflectance of a tarp made of uniform material. The atmospheric correction algorithm used ATCOR-4 based Modran-5 that was widely used for the atmospheric correction of aerial hyperspectral imagery. As a result of analyzing the RMSE of the difference between the reference reflectance and the correction, the vicarious calibration using the tarp in a single period of hyperspectral image was the most accurate, but the atmospheric correction was possible according to the application purpose of using hyperspectral imagery. If the correction process of normalized spectral reflectance is carried out through the additional vicarious calibration for imagery from multiple periods in the future, accurate analysis using hyperspectral drone imagery will be possible.

• Korean Journal of Remote Sensing
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• v.28 no.6
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• pp.623-632
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• 2012

In this paper, we analyzed the effect of wavelet decomposition levels in feature extraction for anomaly detection from hyperspectral imagery. After wavelet analysis, anomaly detection was experimentally performed using the RX detector algorithm to analyze the detecting capabilities. From the experiment for anomaly detection using CASI imagery, the characteristics of extracted features and the changes of their patterns showed that radiance curves were simplified as wavelet transform progresses and H bands did not show significant differences between target anomaly and background in the previous levels. The results of anomaly detection and their ROC curves showed the best performance when using the appropriate sub-band decided from the visual interpretation of wavelet analysis which was L band at the decomposition level where the overall shape of profile was preserved. The results of this study would be used as fundamental information or guidelines when applying wavelet transform to feature extraction and selection from hyperspectral imagery. However, further researches for various anomaly targets and the quantitative selection of optimal decomposition levels are needed for generalization.