• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.149-154
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• 2021

Unlike or common images, Hyperspectral images were taken by continuous electromagnetic spectral into numerous bands according to wavelengths and are high-capacity high-resolution images. It has more information than ordinary images, so it is used to explore objects and materials. To reduce the amount of information in hyper-spectral images to be processed, band selection is utilized. Existing band selection techniques are heuristic techniques based on statistics, which take a long time and often lack generality and universality. To compensate for this, this paper utilizes quantization concept to draw representative bands through Discrete Range, we use them for band selection algorithm. Experimental results showed that the proposed technique performed much faster than conventional band selection methods, and that the performance accuracy was similar to that of the original even though the number of bands was reduced by one-seventh to one-tenth.

• Journal of Conservation Science
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• v.36 no.2
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• pp.73-81
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• 2020

Considering the difficulties associated with the creation of deterioration maps for stone cultural heritages, quantitative determination of chemical and biological contaminants in them is still challenging. Hyperspectral image analysis has been proposed to overcome this drawback. In this study, hyperspectral imaging was performed on Stone Buddhas Temple in Four Directions at Gulbulsa Temple Site(Treasure 121), and several surface contaminants were observed. Based on the color and shape, these chemical and biological contaminants were classified into ten categories. Additionally, a method for establishing each class as a reference image was suggested. Simultaneously, with the help of Spectral Angle Mapper algorithm, two classification methods were used to classify the surface contaminants. Method A focused on the region of interest, while method B involved the application of the spectral library prepared from the image. Comparison of the classified images with the reference image revealed that the accuracies and kappa coefficients of methods A and B were 52.07% and 63.61%, and 0.43 and 0.55, respectively. Additionally, misclassified pixels were distributed in the same contamination series.

• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1265-1274
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• 2021

The analysis of hyperspectral characteristics of materials near the South Han River has been conducted using riverside area measurements by drone installed hyperspectral sensors. Each spectrum reflectance of the riverside materials were compared and analyzed which were consisted of grass, concrete, soil, etc. To verify the drone installed hyperspectral measurements, a ground spectrometer was deployed for field measurements and comparisons for the materials. The comparison results showed that the riverside materials had their unique hyperspectral band characteristics, and the field measurements were similar to the remote sensing data. For the classification of the riverside area, the K-means clustering method and SVM classification method were utilized. The supervised SVM method showed accurate classification of the riverside area than the unsupervised K-means method. Using classification and clustering methods, the inherent spectral characteristic for each material was found to classify the riverside materials of hyperspectral images from drones.

• Journal of the Korea Society of Computer and Information
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• v.20 no.4
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• pp.69-76
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• 2015

We present microscopy based hyperspectral imaging system that successively shows high spatial (micrometer) and temporal resolutions (milisecond), and acquired pseudocolor hemoglobin saturation map a result of various image processing techniques can provide additional information such as oxygen transport, abnormal vascularity and therapeutic effects besides structural and physiological measurements in various diseases. To increase understanding of vascular defects several optical methods of imaging for preclinical/clinical assessment have been developed so far. However, they have some limitations for outcoming resolution and user satisfaction level compared to its cost. A hyperspectral imaging system has shown a wide range of vascular characteristics associated with hypervascularity, aberrant angiogenesis or abnormal vascular remodeling in many diseases. This vascular characteristic is considered as a key component to diagnose and detect a type of disease as evidenced by them.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.622-624
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• 2015

Tumor hypoxia caused by the unique characteristics of solid tumor sites such as lowered vascular density, irregular vasculature, longitudinal oxygen gradient, and unbalanced oxygen consumption has decreased therapeutic efficacy in several clinical trials such as radiation, chemotherapy, and surgery. Hence, tumor oxygenation studies at microvascular levels are important to provide better understanding of the complexity of microvasculature oxygen transport and exchange with tissue. However, polarographic microelectodes, was employed to measure $pO_2$ at the microvasculature level, but it is difficult to perform and does not provide significant spatial and temporal information of oxygen delivery. In this research, we introduce the hyperspectral imaging system able to provide a wide range of vascular characteristics by spatial maps on hemoglobin saturation information for better understanding of the relationship between blood oxygen delivery, hypervascularity, aberrant angiogenesis at microvasculature levels during tumor growth.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.141-147
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• 2021

In this paper, in order to improve the utilization of hyperspectral large-capacity data feature information by reducing complex computations by dimension reduction of neural network inputs in embedded systems, the band selection algorithm is applied in each subset. Among feature extraction and feature selection techniques, the feature selection aim to improve the optimal number of bands suitable for datasets, regardless of wavelength range, and the time and performance, more than others algorithms. Through this experiment, although the time required was reduced by 1/3 to 1/9 times compared to the others band selection technique, meaningful results were improved by more than 4% in terms of performance through the K-neighbor classifier. Although it is difficult to utilize real-time hyperspectral data analysis now, it has confirmed the possibility of improvement.

• Korean Journal of Remote Sensing
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• v.35 no.3
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• pp.375-387
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• 2019

The use of drone-based hyperspectral image offers considerable advantages in high resolution remote sensing applications. The primary objective of this study was to select the optimal bands based on hyperspectral image for the estimation yield of the chinese cabbage. The hyperspectral narrow bands were acquired over 403.36 to 995.19 nm using a 3.97 nm wide, 150 bands, drone-based hyperspectral imaging sensor. Fresh weight data were obtained from 2,031 sample for each field survey. Normalized difference vegetation indices were computed using red, red-edge and near-infrared bands and their relationship with quantitative each fresh weights were established and compared. As a result, predominant proportion of fresh weights are best estimated using data from three narrow bands, in order of importance, centered around 697.29 nm (red band), 717.15 nm (red-edge band) and 808.51 nm (near-infrared band). The study determined three spectral bands that provide optimal chinese cabbage productivity in the visible and near-infrared portion of the spectrum.

• Journal of Korea Water Resources Association
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• v.56 no.2
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• pp.91-101
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• 2023

In this study, we estimated the concentration of chlorophyll-a (Chl-a) using hyperspectral water surface reflectance in an inland weir (Baekjae weir) and estuarine reservoir (Namyang Reservoir) for monitoring the occurrence of algae in freshwater in South Korea. The hyperspectral reflectance was measured by aircraft in Baekjae Weir (BJW) from 2016 to 2017, and a drone in Namyang Reservoir (NYR) from 2020 to 2021. The 30 reflectance bands (BJW: 400-530, 620-680, 710-730, 760-790 nm, NYR: 400-430, 655-680, 740-800 nm) that were highly related to Chl-a concentration were selected using permutation importance. Artificial neural network based Chl-a estimation model was developed using the selected reflectance in both water bodies. And the performance of the model was evaluated with the coefficient of determination (R²), the root mean square error (RMSE), and the mean absolute error (MAE). The performance evaluation results of the Chl-a estimation model for each watershed was R²: 0.63, 0.82, RMSE: 9.67, 6.99, and MAE: 11.25, 8.48, respectively. The developed Chl-a model of this study may be used as foundation tool for the optimal management of freshwater algal blooms in the future.

• Conservation Science in Museum
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• v.28
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• pp.109-136
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• 2022

This study analyzes the , one of the three panels of 《Welcoming Banquets for the Governor of Pyeong-an》, a documentary painting of the late Joseon Dynasty, with the aim to identify the coloring materials used in the painting. The painting was first imaged at each wavelength in order to minimize the potential problems in the process of analyzing specific parts. This study applied X-rays to identify ink, gold, and organic and inorganic pigments and used infrared rays to find ink and copper-based pigments. It also applied hyperspectral imaging to distinguish organic pigments from black, blue, and green materials. It also analyzed spots selected for each color to identify the following materials: white lead (white), ink/indigo (black), a combination of red lead and cinnabar (red), pink dye, purple dye, iron oxides (brown), orpiment/dye (yellow), malachite/malachite and yellow dye/indigo (green), azurite/white lead and indigo/indigo (blue), indigo and cochineal (violet), and gold leaf (gold). It is expected that more efficient analysis will be made possible by securing a sufficient library for each wavelength.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.107-112
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• 2022

In an embedded system such as a drone, it is difficult to store, transfer and analyze the entire hyper-spectral image to a server in real time because it takes a lot of power and time. Therefore, the hyper-spectral image data is transmitted to the server through dimension reduction or compression pre-processing. Feature selection method are used to send only the bands for analysis purpose, and these algorithms usually take a lot of processing time depending on the size of the image, even though the efficiency is high. In this paper, by improving the temporal disadvantage of the band selection algorithm, the time taken 24 hours was reduced to around 60-180 seconds based on the 40000*682 image resolution of 8GB data, and the use of 7.6GB RAM was significantly reduced to 2.3GB using 45 out of 150 bands. However, in terms of pixel classification performance, more than 98% of analysis results were derived similarly to the previous one.