• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.10
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• pp.633-639
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• 2018

High-resolution images using remote sensing (RS) is importance to secure for spatial classification depending on the characteristics of the complex and various factors that make up the river environment. The purpose of this study is to evaluate the accuracy of the classification results and to suggest the possibility of applying the high resolution hyperspectral images obtained by using the drone to perform spatial classification. Hyperspectral images obtained from study area were reduced the dimensionality with PCA and MNF transformation to remove effects of noise. Spatial classification was performed by supervised classifications such as MLC(Maximum Likelihood Classification), SVM(Support Vector Machine) and SAM(Spectral Angle Mapping). In overall, the highest classification accuracy was showed when the MLC supervised classification was used by MNF transformed image. However, it was confirmed that the misclassification was mainly found in the boundary of some classes including water body and the shadowing area. The results of this study can be used as basic data for remote sensing using drone and hyperspectral sensor, and it is expected that it can be applied to a wider range of river environments through the development of additional algorithms.

• 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 the Korean earth science society
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• v.42 no.6
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• pp.623-631
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• 2021

The increased transport of marine hazardous and noxious substances (HNS) has resulted in frequent HNS spill accidents domestically and internationally. There are about 6,000 species of HNS internationally, and most of them have toxic properties. When an accidental HNS spill occurs, it can destroys the marine ecosystem and can damage life and property due to explosion and fire. Constructing a spectral library of HNS according to wavelength and developing a detection algorithm would help prepare for accidents. In this study, a ground HNS spill experiment was conducted in France. The toluene spectrum was determined through hyperspectral sensor measurements. HNS present in the hyperspectral images were detected by applying the spectral mixture algorithm. Preprocessing principal component analysis (PCA) removed noise and performed dimensional compression. The endmember spectra of toluene and seawater were extracted through the N-FINDR technique. By calculating the abundance fraction of toluene and seawater based on the spectrum, the detection accuracy of HNS in all pixels was presented as a probability. The probability was compared with radiance images at a wavelength of 418.15 nm to select abundance fractions with maximum detection accuracy. The accuracy exceeded 99% at a ratio of approximately 42%. Response to marine spills of HNS are presently impeded by the restricted access to the site because of high risk of exposure to toxic compounds. The present experimental and detection results could help estimate the area of contamination with HNS based on hyperspectral remote sensing.

• Korean Journal of Remote Sensing
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• v.30 no.1
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• pp.61-73
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• 2014

This study reviewed an application of water quality estimation using an Airborne Hyperspectral Imagery (A-HSI) and tested a part of Han River water quality (especially suspended solid) estimation with available in-situ data. The estimation of water quality was processed two methods. One is using observation data as downwelling radiance to water surface and as scattering and reflectance into water body. Other is linear regression analysis with water quality in-situ measurement and upwelling data as at-sensor radiance (or reflectance). Both methods drive meaningful results of RS estimation. However it has more effects on the auxiliary dataset as water quality in-situ measurement and water body scattering measurement. The test processed a part of Han River located Paldang-dam downstream. We applied linear regression analysis with AISA eagle hyperspectral sensor data and water quality measurement in-situ data. The result of linear regression for a meaningful band combination shows $-24.847+0.013L_{560}$ as 560 nm in radiance (L) with 0.985 R-square. To comparison with Multispectral Imagery (MSI) case, we make simulated Landsat TM by spectral resampling. The regression using MSI shows -55.932 + 33.881 (TM1/TM3) as radiance with 0.968 R-square. Suspended Solid (SS) concentration was about 3.75 mg/l at in-situ data and estimated SS concentration by A-HIS was about 3.65 mg/l, and about 5.85mg/l with MSI with same location. It shows overestimation trends case of estimating using MSI. In order to upgrade value for practical use and to estimate more precisely, it needs that minimizing sun glint effect into whole image, constructing elaborate flight plan considering solar altitude angle, and making good pre-processing and calibration system. We found some limitations and restrictions such as precise atmospheric correction, sample count of water quality measurement, retrieve spectral bands into A-HSI, adequate linear regression model selection, and quantitative calibration/validation method through the literature review and test adopted general methods.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.46-46
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• 2022

유기물의 복잡한 혼합물인 CDOM(Colored or Chromophoric Dissolved Organic Matter)은 하천 내 BOD(Biological Oxygen Demand), COD(Chemical Oxygen Demand) 및 유기 오염물질과 상당한 관련이 있다. CDOM은 가시광선 영역에서 빛을 흡수하는 성질을 가지고 있으며, 최근 원격감지 기술로 CDOM을 모니터링하기 위한 연구가 진행되고 있다. 본 연구에서는 백제보 상류 23km 구간에서 3년(2016~2018) 중 13일의 초분광영상을 활용하여 머신러닝 기반 CDOM을 추정 알고리즘을 개발하고자 한다. 초분광영상은 400~970 nm의 범위의 4 nm 간격 127개 대역의 분광해상도와 2 m의 공간해상도를 가진 항공기 탑재 AsiaFENIX 초분광 센서를 통해 수집하였으며 CDOM은 Millipore polycarbonate filter (𝚽47, 0.2 ㎛)에서 여과된 CDOM 샘플 자료를 200~800 nm의 흡수계수 스펙트럼으로 추출하여 사용하였다. CDOM 값은 전체기간 동안 2.0~11.0 m^-1의 값 분포를 보였으며 5 m^-1이상의 고농도 구간 자료개수가 전체 153개 샘플자료 중 21개로 불균형하다. 따라서 ADASYN(Adaptive Synthesis Sampling Approach)의 oversampling 방법으로 생성된 합성 데이터를 사용하여 원본 데이터의 소수계층 데이터 불균형을 해결하고 모델 예측 성능을 개선하고자 하였다. 생성된 합성 데이터를 입력변수로 하여 ANN(Artificial Neural Netowk)을 활용한 CDOM 예측 알고리즘을 구축하였다. ADASYN 기법을 통한 합성 데이터는 관측된 데이터의 불균형을 해결하여 기계학습 모델의 CDOM 탐지 성능을 향상시킬 수 있으며, 저수지 내 유기 오염물질 관리를 위한 설계를 지원하는데 사용할 수 있을 것으로 판단된다.

• Ecology and Resilient Infrastructure
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• v.6 no.4
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• pp.243-249
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• 2019

The purpose of this study is to analyze the reflectance of bed materials according to changes in the water level using a drone-based hyperspectral sensor. For this purpose, we took hyperspectral images of bed materials such as soil, gravel, cobble, reed, and vegetation to compare and analyze the spectral data of each material. To adjust the water level, we constructed an experimental channel to control the discharge and installed the bed materials within the channel. In this study, we configured 3 cases according to the water level (0.0 m, 0.3 m, 0.6 m). After the imaging process, we used the mean value of 10 points for each bed material as analytical data. According to the analysis, each material showed a similar reflectance by wavelength and the intrinsic reflectance characteristics of each material were shown in the visible and near-infrared region. Also, the deeper the water level, the lower the peak reflectance in the visible and near-infrared region, and the rate of decrease differed depending on the bed material. We expect the intrinsic properties of these bed materials to be used as basic research data to evaluate river environments in the future.

• Journal of the Korean Association of Geographic Information Studies
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• v.16 no.4
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• pp.16-28
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• 2013

Recently the various applications and spectral indices development of airborne hyperspectral imagery(A-HSI) has been increased. Especially the vegetation indices (VIs) were used to verify stress and vigor of vegetation. The VIs needs two or more spectral bands selectively to calculate as NIR(near infrared) and red wavelength. The A-HIS has specific band characteristics as narrow, continues and many. The A-HIS has narrow, continues and many specific band characteristics. That could be make it confuse which of bands could be explained for appropriate vegetation characteristics. If the A-HIS bands is not the same the wavelength with VIs' development band setting, then it need a selection adequate for spectral characteristics of target vegetation. Therefore we set 4 substitute bands for NIR and red wavelength respectively and calculated two VIs combined with substitute bands such as NDVI(normalized difference vegetation index) and MSRI(modified simple ratio index). To consider the variation of each VIs, we adapted the image differencing method of change detection technique. Also, we used spectral derivative to identify appropriate bands for spectral characteristics of digital forest cover type map. The result of adequate bands for two VIs selected red #3 as 680.2nm and NIR #2 as 801.7nm. This wavelength was good for any forest type in low variations.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1125-1139
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• 2022

In an unmanned aerial vehicles (UAVs) system, a physical offset can be existed between the global positioning system/inertial measurement unit (GPS/IMU) sensor and the observation sensor such as a hyperspectral sensor, and a lidar sensor. As a result of the physical offset, a misalignment between each image can be occurred along with a flight direction. In particular, in a case of multi-sensor system, an observation sensor has to be replaced regularly to equip another observation sensor, and then, a high cost should be paid to acquire a calibration parameter. In this study, we establish a precise sensor model equation to apply for a multiple sensor in common and propose an independent physical offset estimation method. The proposed method consists of 3 steps. Firstly, we define an appropriate rotation matrix for our system, and an initial sensor model equation for direct-georeferencing. Next, an observation equation for the physical offset estimation is established by extracting a corresponding point between a ground control point and the observed data from a sensor. Finally, the physical offset is estimated based on the observed data, and the precise sensor model equation is established by applying the estimated parameters to the initial sensor model equation. 4 region's datasets(Jeon-ju, Incheon, Alaska, Norway) with a different latitude, longitude were compared to analyze the effects of the calibration parameter. We confirmed that a misalignment between images were adjusted after applying for the physical offset in the sensor model equation. An absolute position accuracy was analyzed in the Incheon dataset, compared to a ground control point. For the hyperspectral image, root mean square error (RMSE) for X, Y direction was calculated for 0.12 m, and for the point cloud, RMSE was calculated for 0.03 m. Furthermore, a relative position accuracy for a specific point between the adjusted point cloud and the hyperspectral images were also analyzed for 0.07 m, so we confirmed that a precise data mapping is available for an observation without a ground control point through the proposed estimation method, and we also confirmed a possibility of multi-sensor fusion. From this study, we expect that a flexible multi-sensor platform system can be operated through the independent parameter estimation method with an economic cost saving.

• Korean Journal of Remote Sensing
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• v.35 no.5_1
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• pp.637-647
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• 2019

The detection of crop stress is an important issue for the accurate assessment of yield decline. The photochemical reflectance index (PRI) was developed as a remotely sensed indicator of light use efficiency (LUE). The PRI has been tested in crop stress detection and a number of studies demonstrated the feasibility of using it. However, only few studies have focused on the use of PRI from remote sensing imagery. The monitoring of PRI using drone and satellite is made difficult by the low spectral resolution image captures. In order to estimate PRI from multispectral sensor, we propose a band fusion method using adjacent bands. The method is applied to the drone-based hyperspectral and multispectral imagery and estimated PRI explain 79% of the original PRI. And time series analyses showed that two PRI data (drone-based and SRS sensor) had very similar temporal variations. From these results, PRI from multispectral imagery using band fusion can be used as a new method for evaluation of crop stress.

• Journal of Korea Water Resources Association
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• v.56 no.12
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• pp.919-928
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• 2023

Rainfall characteristics in Korea are concentrated during the summer flood season. In particular, when a large amount of turbid water flows into the dam due to the increasing trend of concentrated rainfall due to abnormal rainfall and abnormal weather conditions, prolonged turbid water phenomenon occurs due to the overturning phenomenon. Much research is being conducted on turbid water prediction to solve these problems. To predict turbid water, turbid water data from the upstream inflow is required, but spatial and temporal data resolution is currently insufficient. To improve temporal resolution, the development of the Turbidity-SS conversion equation is necessary, and to improve spatial resolution, multi-item water quality measurement instrument (YSI), Laser In-Situ Scattering and Transmissometry (LISST), and hyperspectral sensors are needed. Sensor-based measurement can improve the spatial resolution of turbid water by measuring line and surface unit data. In addition, in the case of LISST-200X, it is possible to collect data on particle size, etc., so it can be used in the Turbidity-SS conversion equation for fraction (Clay: Silt: Sand). In addition, among recent remote sensing methods, the spatial distribution of turbid water can be presented when using UAVs with higher spatial and temporal resolutions than other payloads and hyperspectral sensors with high spectral and radiometric resolutions. Therefore, in this study, the Turbidity-SS conversion equation was calculated according to the fraction through laboratory analysis using LISST-200X and YSI-EXO, and sensor-based field measurements including UAV (Matrice 600) and hyperspectral sensor (microHSI 410 SHARK) were used. Through this, the spatial distribution of turbidity and suspended sediment concentration, and the turbidity calculated using the Turbidity-SS conversion equation based on the measured suspended sediment concentration, was presented. Through this, we attempted to review the applicability of the Turbidity-SS conversion equation and understand the current status of turbid water occurrence.