• Journal of Satellite, Information and Communications
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• v.1 no.2
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• pp.1-7
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• 2006

GOCi (Geostationary Ocean Color Imager) is one of the COMS payloads that KARI is currently developing and scheduled to be in operation from around 2008. Its primary objective is to monitor the Korean coastal water environmental condition. We report the current progress in development of the integrated optical model as one of the key analysis tools for the GOCI in-orbit performance verification. The model includes the Sun as the emitting light source. The curved Earth surface section of 2500 km x 2500 km includingthe Korean peninsular os defined as a Lambertian scattering surface consisted of land and sea surface. From its geostationary orbit, the GOCI optical system observes the reflected light from the surfaces with varying reflectance representing the changes in its environmental conditions. The optical ray tracing technique was used to demonstrate the GOCI in-orbit performances such as red tide detection. The computational concept, simulation results and its implications to the on-going development of GOCI are presented.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.7
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• pp.3207-3213
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• 2011

Most methods of color constancy, which is the ability to determine the object color regardless of the scene illuminant, have failed to meet our expectation of their performance especially about low-illuminated scenes. Some methods with high performance need to be developed, but we must, above all else, obtain experimental images for analyzing the required circumstances or evaluating the methods. Therefore, the paper produces new sets of images so that they can be used in the development of color constancy methods suitable for low-illuminated scenes. These sets are composed of two parts: one part of images which are synthesized with spectral power distribution(SPD) of illuminants, spectral reflectance curve of reflectances, and sensor response functions of camera; the other part of images where the intensity of each image is adjusted at the uniform rate. In an experiment, the use of the sets takes an advantage that its result images are analyzed and evaluated quantitatively as their ground truth data are known in advance.

• Korean Journal of Remote Sensing
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• v.37 no.5_2
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• pp.1317-1328
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• 2021

This study analyzes the early satellite mission marine fog detection results from Geostationary Ocean Color Imager-II (GOCI-II). We investigate optical characteristics of the GOCI-II spectral bands for marine fog between October 2020 and March 2021 during the overlapping mission period of Geostationary Ocean Color Imager (GOCI) and GOCI-II. For Rayleigh-corrected reflection (R_rc) at 412 nm band available for the input of the GOCI-II marine fog algorithm, the inter-comparison between GOCI and GOCI-II data showed a small Root Mean Square Error (RMSE) value (0.01) with a high correlation coefficient (0.988). Another input variable, Normalized Localization Standard (NLSD), also shows a reasonable correlation (0.798) between the GOCI and GOCI-II data with a small RMSE value (0.007). We also found distinctive optical characteristics between marine fog and clouds by the GOCI-II observations, showing the narrower distribution of all bands' R_rc values centered at high values for cloud compared to marine fog. The GOCI-II marine fog detection distribution for actual cases is similar to the GOCI but more detailed due to the improved spatial resolution from 500 m to 250 m. The validation with the automated synoptic observing system (ASOS) visibility data confirms the initial reliability of the GOCI-II marine fog detection. Also, it is expected to improve the performance of the GOCI-II marine fog detection algorithm by adding sufficient samples to verify stable performance, improving the post-processing process by replacing real-time available cloud input data and reducing false alarm by adding aerosol information.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.669-681
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• 2023

Studies have tried to apply remote sensing technology, a non-destructive survey method, instead of the existing destructive survey, which requires relatively large labor input and a long time to estimate chlorophyll content, which is an important indicator for evaluating the growth of fruit trees. This study was conducted to non-destructively evaluate the chlorophyll content of pear tree leaves using unmanned aerial vehicle-based hyperspectral imagery for two years(2021, 2022). The reflectance of the single bands of the pear tree canopy extracted through image processing was band rationed to minimize unstable radiation effects depending on time changes. The estimation (calibration and validation) models were developed using machine learning algorithms of elastic-net, k-nearest neighbors(KNN), and support vector machine with band ratios as input variables. By comparing the performance of estimation models based on full band ratios, key band ratios that are advantageous for reducing computational costs and improving reproducibility were selected. As a result, for all machine learning models, when calibration of coefficient of determination (R²)≥0.67, root mean squared error (RMSE)≤1.22 ㎍/cm², relative error (RE)≤17.9% and validation of R²≥0.56, RMSE≤1.41 ㎍/cm², RE≤20.7% using full band ratios were compared, four key band ratios were selected. There was relatively no significant difference in validation performance between machine learning models. Therefore, the KNN model with the highest calibration performance was used as the standard, and its key band ratios were 710/714, 718/722, 754/758, and 758/762 nm. The performance of calibration showed R²=0.80, RMSE=0.94 ㎍/cm², RE=13.9%, and validation showed R²=0.57, RMSE=1.40 ㎍/cm², RE=20.5%. Although the performance results based on validation were not sufficient to estimate the chlorophyll content of pear tree leaves, it is meaningful that key band ratios were selected as a standard for future research. To improve estimation performance, it is necessary to continuously secure additional datasets and improve the estimation model by reproducing it in actual orchards. In future research, it is necessary to continuously secure additional datasets to improve estimation performance, verify the reliability of the selected key band ratios, and upgrade the estimation model to be reproducible in actual orchards.

• Applied Chemistry for Engineering
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• v.31 no.1
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• pp.43-48
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• 2020

Fe₂O₃ nanoparticles with the mixed structure of cubic and nanorod were synthesized by precipitation, hydrothermal, sol-gel method, etching process and heat treatment. Fe₂O₃/TiO₂ core-shell (CS) of type Fe₂O₃@TiO₂ composite was fabricated on a 20 nm nanolayer of TiO₂ coated on the surface of Fe₂O₃ nanoparticles. Fe₂O₃/TiO₂ yolk-shell (YS) composite was prepared by chemical etching and heat treatment of Fe₂O₃/TiO₂ CS nanoparticles. Physical properties of Fe₂O₃, Fe₂O₃@TiO₂ CS and Fe₂O₃@TiO₂ YS nanoparticles were characterized by FE-SEM, HR-TEM and X-ray diffraction. The solar reflectance, commission internationale de l'Elcairage (CIE) color coordinate and heat shield temperatures of Fe₂O₃, CS and YS type Fe₂O₃@TiO₂ pigments filled with poly acrylate (PA) paints were investigated by UV-Vis-NIR spectrometer and homemade heat shield temperature measuring device. The Fe₂O₃@TiO₂ YS red pigment filled PA composite exhibited excellent near infrared light reflecting performance and also reduced the heat shield temperature of 13 ℃ than that of Fe₂O₃ filled counterparts.

• Korean Journal of Food Science and Technology
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• v.40 no.6
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• pp.603-610
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• 2008

The principal objective of current study was to evaluate the potential of near infrared reflectance spectroscopy (NIRS) as a non-destructive method for the prediction of the amylose and protein contents of un-hulled and brown rice in broad-based calibration models. The average amylose and protein content of 75 rice accessions were 20.3% and 7.1%, respectively. Additionally, the range of amylose and protein content were 16.6-24.5% and 3.8-9.3%, respectively. In total, 79 rice germplasms representing a wide range of chemical characteristics, variable physical properties, and origins were scanned via NIRS for calibration and validation equations. The un-hulled and brown rice samples evidenced distinctly different patterns in a wavelength range from 1,440 nm to 2,400 nm in the original NIR spectra. The optimal performance calibration model could be obtained by MPLS (modified partial least squares) using the first derivative method (1:4:4:1) for un-hulled rice and the second derivative method (2:4:4:1) for brown rice. The correlation coefficients $(r^2)$ and standard error of calibration (SEC) of protein and amylose contents for the un-hulled rice were 0.86, 2.48, and 0.84, 1.13, respectively. The $r^2$ and SEC of protein and amylose content for brown rice were 0.95, 1.09 and 0.94, 0.42, respectively. The results of this study suggest that the NIRS technique could be utilized as a routine procedure for the quantification of protein and amylose contents in large accessions of un-hulled rice germplasms.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.53 no.2
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• pp.217-223
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• 2008

Amylose and protein contents are important traits determining the edible quality of rice, especially in East Asian countries. Near-Infrared Reflectance Spectroscopy (NIRS) has become a powerful tool for rapid and nondestructive quantification of natural compounds in agricultural products. To test the practically of using NIRS for estimation of brown rice amylose and protein contents, the spectral reflectances ($400{\sim}2500\;nm$) of total 9,483 accessions of rice germplasm in Rural development Administration (RDA) Genebank ere obtained and compared to chemically determined amylose and protein content. The protein content of tested 119 accessions ranged from 6.5 to 8.0% and 25 accessions exhibited protein contents between 8.5 to 9.5%. In case of amylose content, all tested accessions ranged from 18.1 to 21.7% and the grade from 18.1 to 19.9% includes most number of accessions as 152 and 4 accessions exhibited amylose content between 20.5 to 21.7%. The optimal performance calibration model could be obtained from original spectra of brown rice using MPLS (Modified Partial Least Squares) with the correlation coefficients ($r_2$) for amylose and protein content were 0.865 and 0.786, respectively. The standard errors of calibration (SEC) exhibited good statistic values: 2.078 and 0.442 for amylose and protein contents, respectively. All these results suggest that NIR spectroscopy may serve as reputable and rapid method for quantification of brown rice protein and amylose contents in large numbers of rice germplasm.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1211-1224
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• 2023

Clouds cause many difficult problems in observing land surface phenomena using optical satellites, such as national land observation, disaster response, and change detection. In addition, the presence of clouds affects not only the image processing stage but also the final data quality, so it is necessary to identify and remove them. Therefore, in this study, we developed a new cloud detection technique that automatically performs a series of processes to search and extract the pixels closest to the spectral pattern of clouds in satellite images, select the optimal threshold, and produce a cloud mask based on the threshold. The cloud detection technique largely consists of three steps. In the first step, the process of converting the Digital Number (DN) unit image into top-of-atmosphere reflectance units was performed. In the second step, preprocessing such as Hue-Value-Saturation (HSV) transformation, triangle thresholding, and maximum likelihood classification was applied using the top of the atmosphere reflectance image, and the threshold for generating the initial cloud mask was determined for each image. In the third post-processing step, the noise included in the initial cloud mask created was removed and the cloud boundaries and interior were improved. As experimental data for cloud detection, CAS500-1 L2G images acquired in the Korean Peninsula from April to November, which show the diversity of spatial and seasonal distribution of clouds, were used. To verify the performance of the proposed method, the results generated by a simple thresholding method were compared. As a result of the experiment, compared to the existing method, the proposed method was able to detect clouds more accurately by considering the radiometric characteristics of each image through the preprocessing process. In addition, the results showed that the influence of bright objects (panel roofs, concrete roads, sand, etc.) other than cloud objects was minimized. The proposed method showed more than 30% improved results(F1-score) compared to the existing method but showed limitations in certain images containing snow.

• Journal of Broadcast Engineering
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• v.18 no.1
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• pp.21-30
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• 2013

It is a challenging problem to most of the image processing when the light source is unknown. The color of the light source must be estimated in order to compensate color changes. To estimate the color of the light source, additional assumption is need, so that we assumed color distribution according to the light source. If the pixels, which do not satisfy the assumption, are used, the estimation fails to provide an accurate result. The most popular color distribution assumption is Grey-World Assumption (GWA); it is the assumption that the color in each scene, the surface reflectance averages to gray or achromatic color over the entire images. In this paper, we analyze the characteristics of the camera response function, and the effect of the Grey-World Assumption on the pixel value and chromaticity, based on the inherent characteristics of the light source. Besides, we propose a novel method that detects important pixels for the color estimation of the light source. In our method, we firstly proposed a method that gives weights to pixels satisfying the assumption. Then, we proposed a pixel detection method, which we modified max-RGB method, to apply on the weighted pixels. Maximum weighted pixels in the column direction and row direction in one channel are detected. The performance of our method is verified through demonstrations in several real scenes. Proposed method better accurately estimate the color of the light than previous methods.

• Journal of Biosystems Engineering
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• v.43 no.2
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• pp.148-159
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• 2018

Purpose: Unmanned air vehicle (UAV) remote sensing was applied to test various vegetation indices and make prediction models of protein content of rice for monitoring grain quality and proper management practice. Methods: Image acquisition was carried out by using NIR (Green, Red, NIR), RGB and RE (Blue, Green, Red-edge) camera mounted on UAV. Sampling was done synchronously at the geo-referenced points and GPS locations were recorded. Paddy samples were air-dried to 15% moisture content, and then dehulled and milled to 92% milling yield and measured the protein content by near-infrared spectroscopy. Results: Artificial neural network showed the better performance with $R^2$ (coefficient of determination) of 0.740, NSE (Nash-Sutcliffe model efficiency coefficient) of 0.733 and RMSE (root mean square error) of 0.187% considering all 54 samples than the models developed by PR (polynomial regression), SLR (simple linear regression), and PLSR (partial least square regression). PLSR calibration models showed almost similar result with PR as 0.663 ($R^2$) and 0.169% (RMSE) for cloud-free samples and 0.491 ($R^2$) and 0.217% (RMSE) for cloud-shadowed samples. However, the validation models performed poorly. This study revealed that there is a highly significant correlation between NDVI (normalized difference vegetation index) and protein content in rice. For the cloud-free samples, the SLR models showed $R^2=0.553$ and RMSE = 0.210%, and for cloud-shadowed samples showed 0.479 as $R^2$ and 0.225% as RMSE respectively. Conclusion: There is a significant correlation between spectral bands and grain protein content. Artificial neural networks have the strong advantages to fit the nonlinear problem when a sigmoid activation function is used in the hidden layer. Quantitatively, the neural network model obtained a higher precision result with a mean absolute relative error (MARE) of 2.18% and root mean square error (RMSE) of 0.187%.