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

• Journal of the Korean Geotechnical Society
• /
• v.35 no.11
• /
• pp.51-62
• /
• 2019

In this study, a simple method to estimate the soil water content variation in a wide area was proposed using hyperspectral near-infrared images. The reflectance data of a sand, granite soils, and a kaolinite were measured by reflecting the soil samples with different wavelengths in the visible and near-infrared (VNIR) regions using hyperspectral cameras. The measured reflectances and parameters were used to build a water content prediction model using the Partial Least Square Regression (PLSR) analysis. In the water content prediction model, the Area of Reflectance (Near-infrared, NIR) parameter was the most suitable parameter to determine the water content. The parameter was applicable regardless of the soil type, as the coefficient of determination (R²) exceeded 0.9 for each soil sample. Additionally, the mean absolute percentage error (MAPE) was less than 15% when compared with the actual water content of the soil. Therefore, the predictability of water content variation for soils with water content lower than 50% was confirmed. Accordingly through this study, the predictability of water content variation in several soil types using the hyperspectral near-infrared images was confirmed. For further development, a model that incorporates soil classification would be required to improve the accuracy of the model and to predict higher range of water contents.

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

• Proceedings of the Korea Water Resources Association Conference
• /
• 2018.05a
• /
• pp.239-239
• /
• 2018

위성 및 드론을 이용한 원격탐사 기술이 발전하고 다양한 산출물이 나타남에 따라 수자원 및 하천관리 분야에서 원격탐사기술 활용의 폭이 넓어지고 있다. 원격탐사 영상에서 획득할 수 있는 기본적인 가시영상 이외에도 적외영상, 초분광영상, 수위정보, 레이더 반사도 등을 활용하여 하천정보를 추정하려는 시도가 이루어져왔다. 본 연구에서는 원격탐사 영상에서 획득한 하폭을 기반으로 수리학적 하천유량산정을 기법을 적용하기 위해 등류 및 부등류 해석이 가능한 알고리즘과 프로그램을 개발하였다. 등류해석을 위해 GUI 기반 프로그램을 개발하였으며, 특정 하폭에서의 하천유량을 신속하게 계산할 수 있도록 단면 특성분석 모듈과 운동파 방정식 기반의 Manning 유속공식이 적용되도록 구성하였다. 또한, 부등류해석을 위해 스크립트기반의 프로그램을 개발하였다. 부등류 해석 프로그램에는 하천유량을 경계조건으로 수위를 계산하는 일반적인 부등류 수면곡선식의 해석절차를 하폭기반 원격하천 유량 산정 목적에 맞게 재구성하여 원격탐사기술로 획득한 하폭을 경계조건으로 하천 유량을 시산하는 알고리즘을 구현하였다. 본 프로그램을 활용하여 한국건설기술연구원 하천실험센터에서 드론을 이용한 등류/부등류 실험결과를 해석하였으며, 등류 흐름조건에서는 두 가지 해석방법 각각 14.3%, 14.7%의 유사한 평균상대오차를 나타내었고 부등류 흐름조건에서는 등류해석은 62.6%, 부등류해석은 15.8%의 평균상대오차를 나타내었다. 실규모하천에서의 원격하천유량 산정을 위해서는 등류기반 해석방법에 비해 비균일 하천단면에서의 부등류 현상을 모의할 수 있는 하폭기반 부등류 수면곡선의 시산 알고리즘이 유리한 것으로 판단된다.

• Journal of Korea Water Resources Association
• /
• v.55 no.8
• /
• pp.623-634
• /
• 2022

Due to river maintenance projects such as the creation of hydrophilic areas around rivers and the Four Rivers Project, the flow characteristics of rivers are continuously changing, and the risk of water quality accidents due to the inflow of various pollutants is increasing. In the event of a water quality accident, it is necessary to minimize the effect on the downstream side by predicting the concentration and arrival time of pollutants in consideration of the flow characteristics of the river. In order to track the behavior of these pollutants, it is necessary to calculate the diffusion coefficient and dispersion coefficient for each section of the river. Among them, the dispersion coefficient is used to analyze the diffusion range of soluble pollutants. Existing experimental research cases for tracking the behavior of pollutants require a lot of manpower and cost, and it is difficult to obtain spatially high-resolution data due to limited equipment operation. Recently, research on tracking contaminants using RGB drones has been conducted, but RGB images also have a limitation in that spectral information is limitedly collected. In this study, to supplement the limitations of existing studies, a hyperspectral sensor was mounted on a remote sensing platform using a drone to collect temporally and spatially higher-resolution data than conventional contact measurement. Using the collected spatio-temporal hyperspectral images, the tracer concentration was calculated and the transverse dispersion coefficient was derived. It is expected that by overcoming the limitations of the drone platform through future research and upgrading the dispersion coefficient calculation technology, it will be possible to detect various pollutants leaking into the water system, and to detect changes in various water quality items and river factors.

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

• Journal of Korea Water Resources Association
• /
• v.53 no.10
• /
• pp.845-859
• /
• 2020

Climate change and recent heat waves have drawn public attention toward other environmental issues, such as water pollution in the form of algal blooms, chemical leaks, and oil spills. Water pollution by the leakage of chemicals may severely affect human health as well as contaminate the air, water, and soil and cause discoloration or death of crops that come in contact with these chemicals. Chemicals that may spill into water streams are often colorless and water-soluble, which makes it difficult to determine whether the water is polluted using the naked eye. When a chemical spill occurs, it is usually detected through a simple contact detection device by installing sensors at locations where leakage is likely to occur. The drawback with the approach using contact detection sensors is that it relies heavily on the skill of field workers. Moreover, these sensors are installed at a limited number of locations, so spill detection is not possible in areas where they are not installed. Recently hyperspectral images have been used to identify land cover and vegetation and to determine water quality by analyzing the inherent spectral characteristics of these materials. While hyperspectral sensors can potentially be used to detect chemical substances, there is currently a lack of research on the detection of chemicals in water streams using hyperspectral sensors. Therefore, this study utilized remote sensing techniques and the latest sensor technology to overcome the limitations of contact detection technology in detecting the leakage of hazardous chemical into aquatic systems. In this study, we aimed to determine whether 18 types of hazardous chemicals could be individually classified using hyperspectral image. To this end, we obtained hyperspectral images of each chemical to establish a spectral library. We expect that future studies will expand the spectral library database for hazardous chemicals and that verification of its application in water streams will be conducted so that it can be applied to real-time monitoring to facilitate rapid detection and response when a chemical spill has occurred.

• Journal of Korean Society for Geospatial Information Science
• /
• v.23 no.3
• /
• pp.3-10
• /
• 2015

All kinds of objects on the ground have inherent spectral reflectance curves, which can be used to classify the ground objects and to detect the target. Remotely sensed data have to be transferred to spectral reflectance for accurate analysis. There are formula methods provided by the institution, mathematical model method and ground-data-based method. In this study, RapidEye satellite image was converted to reflectance data using spectral reflectance of a CASI hyperspectral image by using vicarious radiometric calibration. The results were compared with those of the other calibration methods and ground data. The proposed method was closer to the ground data than ATCOR and New Kurucz 2005 method and equal with ELM method.

• Journal of Bio-Environment Control
• /
• v.27 no.1
• /
• pp.34-39
• /
• 2018

This research was conducted to estimate moisture content in cucurbitaceae seedlings, such as cucumber and watermelon, using hyperspectral imagery. Using a hyperspectral image acquisition system, the reflectance of leaf area of cucumber and watermelon seedlings was calculated after providing water stress. Then, moisture content in each seedling was measured by using a dry oven. Finally, using reflectance and moisture content, the moisture content estimation models were developed by PLSR analysis. After developing the estimation models, performance of the cucumber showed 0.73 of $R^2$, 1.45% of RMSE, and 1.58% of RE. Performance of the watermelon showed 0.66 of $R^2$, 1.06% of RMSE, and 1.14% of RE. The model performed slightly better after removing one sample from cucumber seedlings as outlier and unnecessary. Hence, the performance of new model for cucumber seedlings showed 0.79 of $R^2$, 1.10% of RMSE, and 1.20% of RE. The model performance combined with all samples showed 0.67 of $R^2$, 1.26% of RMSE, and 1.36% of RE. The model of cucumber showed better performance than the model of watermelon. This is because variables of cucumber are consisted of widely distributed variation, and it affected the performance. Further, accuracy and precision of the cucumber model were increased when an insignificant sample was eliminated from the dataset. Finally, it is considered that both models can be significantly used to estimate moisture content, as gradients of trend line are almost same and intersected. It is considered that the accuracy and precision of the estimating models possibly can be improved, if the models are constructed by using variables with widely distributed variation. The improved models will be utilized as the basis for developing low-priced sensors.

• Journal of the Korean Society of Industry Convergence
• /
• v.26 no.5
• /
• pp.785-792
• /
• 2023

Because hyperspectral imaging provides detailed spectral information across a broad range of wavelengths, it can be utilized in numerous applications, including environmental monitoring, food quality inspection, medical diagnosis, material identification, art authentication, and crime scene analysis. However, hyperspectral images often contain various types of distortions due to the environmental conditions during image acquisition, which necessitates the proper removal of these distortions through a data preprocessing process. In this study, a preprocessing method was investigated to effectively correct the distortion caused by artificial light sources used in indoor hyperspectral imaging. For this purpose, a halogen-tungsten artificial light source was installed indoors, and hyperspectral images were acquired. The acquired images were then corrected for distortion using a preprocessing that does not require complex auxiliary equipment. After the corrections were made, the results were analyzed. According to the analysis, a statistical transformation technique using mean and standard deviation with reference to a reference signal was found to be the most effective in correcting distortions caused by artificial light sources.