• Korean Journal of Remote Sensing
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• v.30 no.6
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• pp.809-816
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• 2014

Ocean Color products have been used to understand marine ecosystem. In high latitude region, ice melting optically influences the ocean color products. In this study, we assessed optical properties in fjord around Svalbard Arctic sea, and estimated distribution of chlorophyll-a and suspended sediment by using high resolution satellite data, Landsat-8 Operational Land Imager (OLI). To estimate chlorophyll-a and suspended sediment concentrations, various regression models were tested with different band ratio. The regression models were not shown high correlation because of temporal difference between satellite data and in-situ data. However, model-derived distribution of ocean color products from OLI showed a possibility that fjord and coastal areas around Arctic Sea can be monitored with high resolution satellite data. To understand climate change pattern around Arctic Sea, we need to understand ice meting influences on marine ecosystem change. Results of this study will be used to high resolution monitoring of ice melting and its influences on the marine ecosystem change at high latitude. KOPRI (Korea Polar Research Institute) has been operated the Dasan station on Svalbard since 2002, and study was conducted using Arctic station.

• 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 the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.6_1
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• pp.519-528
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• 2012

It was pointed out that the terrain distortion of SAR image is even worse than that of optical image although SAR imagery has the advantages of being independent of solar illumination and weather conditions. It is thus necessary to correct terrain distortion in SAR image for various application areas to integrate SAR and optical image information. There has to be a clear evaluation of terrain correction of high resolution SAR image according to the quality of DEM because the DEM of study site is generally used in the process of terrain correction. To achieve this issue, this paper compared the effects of quality of Digital Elevation Model(DEM) in the process of terrain correction of high resolution SAR images, using the DEM produced from 1:5000 topographic contour maps, LiDAR DEM, ASTER GDEM, SRTM DEM. We used TerraSAR-X and Cosmo-SkyMed, as the test data set, which are constructed on the same X-band SAR system as KOMPSAT-5. In order to evaluate quantitatively the correction results, we conducted comparative evaluation with the KOMPSAT-2 ortho image of the same region. The evaluation results showed that the DEM produced from 1:5000 topographic contour maps achieved successful results in the terrain correction of SAR image compared with the other DEM data, and the widely used SRTM DEM data in various applications was not suitable for the terrain correction of high resolution SAR images.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.901-912
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• 2022

The frequency of disasters is increasing due to global climate change, and unusual heavy rains and rainy seasons are occurring in Korea. Periodic monitoring and rapid detection are important because these weather conditions can lead to drought and flooding, causing secondary damage. Although research using optical images is continuously being conducted to determine the waterbody, there is a limitation in that it is difficult to detect due to the influence of clouds in order to detect floods that accompany heavy rain. Therefore, there is a need for research using synthetic aperture radar (SAR) that can be observed regardless of day or night in all weather. In this study, using Sentinel-1 SAR images that can be collected in near-real time as open data, the UNet model among deep learning algorithms that have recently been used in various fields was applied. In previous studies, waterbody detection studies using SAR images and deep learning algorithms are being conducted, but only a small number of studies have been conducted in Korea. In this study, to determine the applicability of deep learning of SAR images, UNet and the existing algorithm thresholding method were compared, and five indices and Sentinel-2 normalized difference water index (NDWI) were evaluated. As a result of evaluating the accuracy with intersect of union (IoU), it was confirmed that UNet has high accuracy with 0.894 for UNet and 0.699 for threshold method. Through this study, the applicability of deep learning-based SAR images was confirmed, and if high-resolution SAR images and deep learning algorithms are applied, it is expected that periodic and accurate waterbody change detection will be possible in Korea.

• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1173-1183
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• 2023

Over the past 20 years, Korea's overall import and export cargo volume has increased at an average annual rate of approximately 5.3%. About 99% of the cargo is still being transported by sea. Due to recent increases in maritime cargo volume, congestion in maritime logistics has become challenging due to factors such as the COVID-19 pandemic and conflicts. Continuous monitoring of ports has become crucial. Various ground observation systems and Automatic Identification System (AIS) data have been utilized for monitoring ports and conducting numerous preliminary studies for the efficient operation of container terminals and cargo volume prediction. However, small and developing countries' ports face difficulties in monitoring due to environmental issues and aging infrastructure compared to large ports. Recently, with the increasing utility of artificial satellites, preliminary studies have been conducted using satellite imagery for continuous maritime cargo data collection and establishing ocean monitoring systems in vast and hard-to-reach areas. This study aims to visually detect ships docked at berths in the Busan New Port using high-resolution satellite imagery and quantitatively evaluate berth utilization rates. By utilizing high-resolution satellite imagery from Compact Advanced Satellite 500-1 (CAS500-1), Korea Multi-Purpose satellite-3 (KOMPSAT-3), PlanetScope, and Sentinel-2A, ships docked within the port berths were visually detected. The berth utilization rate was calculated using the total number of ships that could be docked at the berths. The results showed variations in berth utilization rates on June 2, 2022, with values of 0.67, 0.7, and 0.59, indicating fluctuations based on the time of satellite image capture. On June 3, 2022, the value remained at 0.7, signifying a consistent berth utilization rate despite changes in ship types. A higher berth utilization rate indicates active operations at the berth. This information can assist in basic planning for new ship operation schedules, as congested berths can lead to longer waiting times for ships in anchorages, potentially resulting in increased freight rates. The duration of operations at berths can vary from several hours to several days. The results of calculating changes in ships at berths based on differences in satellite image capture times, even with a time difference of 4 minutes and 49 seconds, demonstrated variations in ship presence. With short observation intervals and the utilization of high-resolution satellite imagery, continuous monitoring within ports can be achieved. Additionally, utilizing satellite imagery to monitor changes in ships at berths in minute increments could prove useful for small and developing country ports where harbor management is not well-established, offering valuable insights and solutions.

• New Physics: Sae Mulli
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• v.68 no.12
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• pp.1281-1287
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• 2018

In this study, $Al_xGa_{1-x}N$ films were grown on (0001) sapphire substrates by using metal-organic chemical vapor deposition (MOCVD). The crystallinity of the grown films was examined with X-ray diffraction (XRD) patterns. The surfaces and the chemical properties of the $Al_xGa_{1-x}N$ films were investigated using atomic force microscopy (AFM) and X-ray photoelectron spectroscopy (XPS), respectively. The optical properties of the $Al_xGa_{1-x}N$ film were studied in a wide photon energy range between 2.0 ~ 8.7 eV by using spectroscopic ellipsometry (SE) at room temperature. The data obtained by using SE were analyzed to find the critical points of the pseudodielectric function spectra, $<{\varepsilon}(E)>=<{\varepsilon}_1(E)>+i<{\varepsilon}_2(E)>$. In addition, the second derivative spectra, $d^2<{\varepsilon}(E)>/dE^2$, of the pseudodielectric function for the $Al_xGa_{1-x}N$ films were numerically calculated to determine the critical points (CPs), such as the $E_0$, $E_1$, and $E_2$ structure. For the four samples (x = 0.18, 0.21, 0.25, 0.29) between a composition of x = 0.18 and x = 0.29, changes in the critical points (blue-shifts) with increasing Al composition at 300 K for the $Al_xGa_{1-x}N$ film were observed via ellipsometric measurements for the first time.

• Journal of Internet Computing and Services
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• v.22 no.6
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• pp.25-32
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• 2021

In line with future changes in the marine environment, Aids to Navigation has been used in various fields and their use is increasing. The term "Aids to Navigation" means an aid to navigation prescribed by Ordinance of the Ministry of Oceans and Fisheries which shows navigating ships the position and direction of the ships, position of obstacles, etc. through lights, shapes, colors, sound, radio waves, etc. Also now the use of Aids to Navigation is transforming into a means of identifying and recording the marine weather environment by mounting various sensors and cameras. However, Aids to Navigation are mainly lost due to collisions with ships, and in particular, safety accidents occur because of poor observation visibility due to sea fog. The inflow of sea fog poses risks to ports and sea transportation, and it is not easy to predict sea fog because of the large difference in the possibility of occurrence depending on time and region. In addition, it is difficult to manage individually due to the features of Aids to Navigation distributed throughout the sea. To solve this problem, this paper aims to identify the marine weather environment by estimating sea fog level approximately with images taken by cameras mounted on Aids to Navigation and to resolve safety accidents caused by weather. Instead of optical and temperature sensors that are difficult to install and expensive to measure sea fog level, sea fog level is measured through the use of general images of cameras mounted on Aids to Navigation. Furthermore, as a prior study for real-time sea fog level estimation in various seas, the sea fog level criteria are presented using the Haze Model and Dark Channel Prior. A specific threshold value is set in the image through Dark Channel Prior(DCP), and based on this, the number of pixels without sea fog is found in the entire image to estimate the sea fog level. Experimental results demonstrate the possibility of estimating the sea fog level using synthetic haze image dataset and real haze image dataset.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.925-938
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• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.