• Korean Journal of Geomatics
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• v.3 no.1
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• pp.59-65
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• 2003

The merging of different scales or multi-sensor image data is becoming a widely used procedure of the complementary nature of various data sets. Ideally, the merging method should not distort the characteristics of the high-spatial and high-spectral resolution data used. To present an effective merging method for survey of reclaimed land using the high-resolution (6.6 m) Electro-Optical Camera (EOC) panchromatic image of the first Korea Multi-Purpose Satellite 1 (KOMPSA T-l) and the multispectral Moderate Resolution Imaging Spectroradiometer (MODIS) image data, this paper compares the results of Intensity Hue Saturation (IHS) and Principal Component Analysis (PCA) methods. The comparison is made by statistical and visual evaluation of three-color combination images of IHS and PCA results based on spatial and spectral characteristics. The use of MODIS bands 1, 2, and 3 with a contrast stretched EOC panchromatic image as a substitute for intensity was found to be particularly effective in this study.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.4
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• pp.86-101
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• 2019

Remote sensing technology has been continuously developed both quantitatively and qualitatively, including platform development, exploration area, and exploration functions. Recently, the use cases and related researches in the agricultural field are increasing. Also, since it is possible to detect and quantify the condition of cropland and establish management plans and policy support for cropland and agricultural environment, it is being studied in various fields such as crop growth abnormality determination and crop estimation based on time series information. The purpose of this study was to analyze the vegetation index for agricultural land reclamation area using a UAV equipped with a multi-spectral sensor. In addition, field surveys were conducted to evaluate the accuracy of vegetation indices calculated from multispectral image data obtained using UAV. The most appropriate vegetation index was derived by evaluating the correlation between vegetation index calculated by field survey and vegetation index calculated from UAV multispectral image, and was used to analyze vegetation index of the entire area.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.409-414
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• 1998

To examine the detectability of the aerosol and/or Yellow dust from China crossing over the Yellow sea, three works carried out as follows , Firstly, a comparison was made of the visible(VIS), water vapor(WV), and Infrared(IR) images of the GMS-5 and NOAA/AVHRR on the cases of yellow sand event over Korea. Secondly, the spectral radiance and reflectance(%) was observed during the yellow sand phenomena on April, 1998 in Seoul using the GER-2600 spectroradiometer, which observed the reflected radiance from 350 to 2500 nm in the atmosphere. We selected the optimum wavelength for detecting of the yellow sand from this observation, considering the effects of atmospheric absorption. Finally, the atmospheric radiance emerging from the LOWTRAN-7 radiative transfer model was simulated with and without yellow sand, where we used the estimated aerosol column optical depth ($\tau$ 673 nm) in the Meteorological Research Institute and the d'Almeida's statistical atmospheric aerosol radiative characteristics. The image analysis showed that it was very difficult to detect the yellow sand region only by the image processing because the albedo characteristics of the sand vary irregularly according to the density, size, components and depth of the yellow sand clouds. We found that the 670-680 nm band was useful to simulate aerosol characteristics considering the absorption band from the radiance observation. We are now processing the simulation of atmospheric radiance distribution in the range of 400-900 nm. The purpose of this study is to present the preliminary results of the aerosol and/or Yellow dust detectability using the Ocean Scanning Multispectral Imager(OSMI), which will be mounted on KOMPSAT-1 as the ocean color monitoring sensor with the range of 400-900 nm wavelength.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.110-112
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• 2003

The researchers, who seek geological and environmental information, depend on the remote sensing and aerial photographic datum from various commercial satellites and aircraft. However, the adverse weather conditions and the expensive equipment can restrict that the researcher can collect their data anywhere and any time. To allow for better flexibility, we have developed a compact, a multispectral automatic Aerial photographic system. This system's Multi-spectral camera can catch the visible (RGB) and infrared (NIR) bands (3032${\ast}$2008 pixel) image. Our system consists of a thermal infrared camera and automatic balance control, and it managed and controlled by a palm-top computer. And it includes a camera gimbals system, GPS receiver, weather sensor and etc. As a result, we have successfully tested its ability to acquire aerial photography, weather data, as well as GPS data, making it a very flexible tool for environmental data monitoring.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.4
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• pp.275-292
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• 2022

In order to detect climate changes using satellite imagery, the GCOS (Global Climate Observing System) defines requirements such as spatio-temporal resolution, stability by the time change, and uncertainty. Due to limitation of GK-2A sensor performance, the level-2 products can not satisfy the requirement, especially for spatial resolution. In this paper, we found the optimal pan-sharpening algorithm for GK-2A products. The six pan-sharpening methods included in CS (Component Substitution), MRA (Multi-Resolution Analysis), VO (Variational Optimization), and DL (Deep Learning) were used. In the case of DL, the synthesis property based method was used to generate training dataset. The process of synthesis property is that pan-sharpening model is applied with Pan (Panchromatic) and MS (Multispectral) images with reduced spatial resolution, and fused image is compared with the original MS image. In the synthesis property based method, fused image with desire level for user can be produced only when the geometric characteristics between the PAN with reduced spatial resolution and MS image are similar. However, since the dissimilarity exists, RD (Random Down-sampling) was additionally used as a way to minimize it. Among the pan-sharpening methods, PSGAN was applied with RD (PSGAN_RD). The fused images are qualitatively and quantitatively validated with consistency property and the synthesis property. As validation result, the GSA algorithm performs well in the evaluation index representing spatial characteristics. In the case of spectral characteristics, the PSGAN_RD has the best accuracy with the original MS image. Therefore, in consideration of spatial and spectral characteristics of fused image, we found that PSGAN_RD is suitable for GK-2A products.

• Journal of Broadcast Engineering
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• v.16 no.3
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• pp.561-565
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• 2011

This paper presents an imaging system for reconstruction of enhanced color images using the conventional Bayer CFA. By extracting various colors such as RGBCY from two sequential images which consist of a image by broadband G channel lens filter and the other image captured without one, the proposed color image reconstruction system can reduce the computational complexity for demosaicking and make high resolution color information without aliasing artifacts. Because the proposed system uses the common Bayer CFA image sensor, fabricating a new type of CFA is not necessary for obtaining a multi-spectral image, which can be easily extensible for applications of multi-spectral imaging. Finally, in order to verify the performance of the proposed system, experimental results are performed. By comparing with the existing demosaicking methods, the proposed camera system showed the significant improvements in the sense of color resolution.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.108-108
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• 2022

In this study, multispectral images of wheat according to soil water state were collected, compared, and analyzed to measure the physiological response of crops to environmental stress at the individual level. CMS-V multi-spectral camera(Silios Technologies) was used for image acquisition. The camera lens consists of eight spectral bands between 550nm and 830nm. Light Reflective information collected in each band sensor and stored in digital values, and it is converted into a reflectance for calculating the vegetation index and used. According to the camera manual, the NDVI(Normalized Difference vegetation index) value was calculated using 628 nm and 752 nm bands. Image measurement was conducted under natural light conditions, and reflectance standards(Labsphere) were captured with plants for reflectance calculation. The wheat variety used Gosomil, and the wheat grown in the field was transplanted into a pot after heading date and measured. Three treatments were performed so that the soil volumetric water content of the pot was 13~17%, 20~23%, and 25%, and the growth response of wheat according to each treatment was compared using the NDVI value. In the first measurement after port transplantation, the difference in NDVI value according to treatment was not significant, but in the subsequent measurement, the NDVI value of the treatment with a water content of 13 to 17% was lowest and was the highest at 20 to 23%. The NDVI values decreased compared to the first measurement in all treatment, and the decrease was the largest at 13-17% water content and the smallest at 20-23%. Although the difference in NDVI values could be confirmed, it would be difficult to directly relate it to the water stress of plants, and further research on the response of crops to environmental stress and the analysis of multi-spectral image will be needed.

• Journal of Korean Society of Rural Planning
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• v.30 no.1
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• pp.57-66
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• 2024

This study aimed to develop a precise vegetation cover classification model for small streams using the combination of drone remote sensing and support vector machine (SVM) techniques. The chosen study area was the Idong stream, nestled within Geosan-gun, Chunbuk, South Korea. The initial stage involved image acquisition through a fixed-wing drone named ebee. This drone carried two sensors: the S.O.D.A visible camera for capturing detailed visuals and the Sequoia+ multispectral sensor for gathering rich spectral data. The survey meticulously captured the stream's features on August 18, 2023. Leveraging the multispectral images, a range of vegetation indices were calculated. These included the widely used normalized difference vegetation index (NDVI), the soil-adjusted vegetation index (SAVI) that factors in soil background, and the normalized difference water index (NDWI) for identifying water bodies. The third stage saw the development of an SVM model based on the calculated vegetation indices. The RBF kernel was chosen as the SVM algorithm, and optimal values for the cost (C) and gamma hyperparameters were determined. The results are as follows: (a) High-Resolution Imaging: The drone-based image acquisition delivered results, providing high-resolution images (1 cm/pixel) of the Idong stream. These detailed visuals effectively captured the stream's morphology, including its width, variations in the streambed, and the intricate vegetation cover patterns adorning the stream banks and bed. (b) Vegetation Insights through Indices: The calculated vegetation indices revealed distinct spatial patterns in vegetation cover and moisture content. NDVI emerged as the strongest indicator of vegetation cover, while SAVI and NDWI provided insights into moisture variations. (c) Accurate Classification with SVM: The SVM model, fueled by the combination of NDVI, SAVI, and NDWI, achieved an outstanding accuracy of 0.903, which was calculated based on the confusion matrix. This performance translated to precise classification of vegetation, soil, and water within the stream area. The study's findings demonstrate the effectiveness of drone remote sensing and SVM techniques in developing accurate vegetation cover classification models for small streams. These models hold immense potential for various applications, including stream monitoring, informed management practices, and effective stream restoration efforts. By incorporating images and additional details about the specific drone and sensors technology, we can gain a deeper understanding of small streams and develop effective strategies for stream protection and management.

• Korean Journal of Remote Sensing
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• v.24 no.2
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• pp.213-222
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• 2008

Although hyperspectral sensing data have shown great potential to derive various surface information that is not usually available from conventional multispectral image, the acquisition of proper hyperspectral image data are often limited. To use ground-based hyperspectral camera image for remote sensing studies, radiometric calibration should be prerequisite. The objective of this study is to develop radiometric calibration procedure to convert image digital number (DN) value to surface reflectance for the 120 bands ground-based hyperspectral camera. Hyperspectral image and spectral measurements were simultaneously obtained from the experimental target that includes 22 different surface materials of diverse spectral characteristics at wavelength range between 400 to 900 nm. Calibration coefficients to convert image DN value to at-sensor radiance were initially derived from the regression equations between the sample image and spectral measurements using ASD spectroradiometer. Assuming that there is no atmospheric effects when the image acquisition and spectral measurements were made at very close distance in ground, we were also able to derive calibration coefficients that directly transform DN value to surface reflectance. However, these coefficients for deriving reflectance values should not be applied when the camera is used for aerial image that contains significant effect from atmosphere and further atmospheric correction procedure is required in such case.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.1
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• pp.101-119
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• 2022

Existing algal bloom monitoring is based on field sampling, and there is a limit to understanding the spatial distribution of algal blooms, such as the occurrence and spread of algae, due to local investigations. In this study, algal bloom monitoring was performed using an unmanned aerial vehicle and multispectral sensor, and data on the distribution of algae were provided. For the algal bloom monitoring site, data were acquired from the Mulgeum·Mae-ri site located in the lower part of the Nakdong River, which is the areas with frequent algal bloom. The Chlorophyll-a(Chl-a) value of field-collected samples and the Chl-a estimation formula derived from the correlation between the spectral indices were comparatively analyzed. As a result, among the spectral indices, Maximum Chlorophyll Index (MCI) showed the highest statistical significance(R²=0.91, RMSE=8.1mg/m³). As a result of mapping the distribution of algae by applying MCI to the image of August 05, 2021 with the highest Chl-a concentration, the river area was 1.7km², the Warning area among the indicators of the algal bloom warning system was 1.03km²(60.56%) and the Algal Bloom area occupied 0.67km²(39.43%). In addition, as a result of calculating the number of occurrence days in the area corresponding to the "Warning" in the images during the study period (July 01, 2021~November 01, 2021), the Chl-a concentration above the "Warning" level was observed in the entire river section from 12 to 19 times. The algal bloom monitoring method proposed in this study can supplement the limitations of the existing algal bloom warning system and can be used to provide information on a point-by-point basis as well as information on a spatial range of the algal bloom warning area.