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Landsat 위성 영상으로부터 Modified U-Net을 이용한 백두산 천지 얼음변화도 관측
이어루,이하성,박순천,정형섭,Lee, Eu-Ru,Lee, Ha-Seong,Park, Sun-Cheon,Jung, Hyung-Sup 대한원격탐사학회 2022 大韓遠隔探査學會誌 Vol.38 No.6
Cheonji Lake, the caldera of Baekdu Mountain, located on the border of the Korean Peninsula and China, alternates between melting and freezing seasonally. There is a magma chamber beneath Cheonji, and variations in the magma chamber cause volcanic antecedents such as changes in the temperature and water pressure of hot spring water. Consequently, there is an abnormal region in Cheonji where ice melts quicker than in other areas, freezes late even during the freezing period, and has a high-temperature water surface. The abnormal area is a discharge region for hot spring water, and its ice gradient may be used to monitor volcanic activity. However, due to geographical, political and spatial issues, periodic observation of abnormal regions of Cheonji is limited. In this study, the degree of ice change in the optimal region was quantified using a Landsat -5/-7/-8 optical satellite image and a Modified U-Net regression model. From January 22, 1985 to December 8, 2020, the Visible and Near Infrared (VNIR) band of 83 Landsat images including anomalous regions was utilized. Using the relative spectral reflectance of water and ice in the VNIR band, unique data were generated for quantitative ice variability monitoring. To preserve as much information as possible from the visible and near-infrared bands, ice gradient was noticed by applying it to U-Net with two encoders, achieving good prediction accuracy with a Root Mean Square Error (RMSE) of 140 and a correlation value of 0.9968. Since the ice change value can be seen with high precision from Landsat images using Modified U-Net in the future may be utilized as one of the methods to monitor Baekdu Mountain's volcanic activity, and a more specific volcano monitoring system can be built.
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TerraSAR-X, Sentinel-1, ALOS PALSAR-2 위성레이더 영상을 활용한 천지호 GeoAI 데이터셋
이어루,이하성,이지민,박순천,정형섭 (사)지오에이아이데이터학회 2023 GEO DATA Vol.5 No.4
The fluctuations in the area and level of Cheonji in Baekdu Mountain have been employed as significant indicators of volcanic activity. Monitoring these changes directly in the field is challenging because of the geographical and spatial features of Baekdu Mountain. Therefore, remote sensing technology is crucial. Synthetic aperture radar utilizes high-transmittance microwaves to directly emit and detect the backscattering from objects. This weatherproof approach allows monitoring in every climate. Additionally, it can accurately differentiate between water bodies and land based on their distinct roughness and permittivity characteristics. Therefore, satellite radar is highly suitable for monitoring the water area of Cheonji. The existing algorithms for classifying water bodies using satellite radar images are significantly impacted by speckle noise and shadows, resulting in frequent misclassification. Deep learning techniques are being utilized in algorithms to accurately compute the area and boundary of interest in an image, surpassing the capabilities of previous algorithms. This study involved the creation of an AI dataset specifically designed for detecting water bodies in Cheonji. The dataset was constructed using satellite radar images from TerraSAR-X, Sentinel-1, and ALOS-2 PALSAR-2. The primary objective was to accurately detect the area and level of water bodies. Applying the dataset of this study to deep learning techniques for ongoing monitoring of the water bodies and water levels of Cheonji is anticipated to significantly contribute to a systematic method for monitoring and forecasting volcanic activity in Baekdu Mountain.
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Sentinel-1 SAR 영상을 활용한국내 내륙 수체 학습 데이터셋 구축 및 알고리즘 적용 연구
이어루,정형섭 대한원격탐사학회 2023 大韓遠隔探査學會誌 Vol.39 No.6
Floods are becoming more severe and frequent due to global warming-induced climatechange. Water disasters are rising in Korea due to severe rainfall and wet seasons. This makes preventiveclimate change measures and efficient water catastrophe responses crucial, and synthetic aperture radarsatellite imagery can help. This research created 1,423 water body learning datasets for individual waterbody regions along the Han and Nakdong waterways to reflect domestic water body properties discoveredby Sentinel-1 satellite radar imagery. We created a document with exact data annotation criteria for manysituations. After the dataset was processed, U-Net, a deep learning model, analyzed water body detectionresults. The results from applying the learned model to water body locations not involved in the learningprocess were studied to validate soil water body monitoring on a national scale. The analysis showedthat the created water body area detected water bodies accurately (F1-Score: 0.987, Intersection overUnion [IoU]: 0.955). Other domestic water body regions not used for training and evaluation showedsimilar accuracy (F1-Score: 0.941, IoU: 0.89). Both outcomes showed that the computer accuratelyspotted water bodies in most areas, however tiny streams and gloomy areas had problems. This workshould improve water resource change and disaster damage surveillance. Future studies will likely includemore water body attribute datasets. Such databases could help manage and monitor water bodiesnationwide and shed light on misclassified regions.
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TerraSAR-X 영상으로부터 Modified U-NET을 이용한 홍수 매핑
유진우,윤영웅,이어루,백원경,정형섭,Yu, Jin-Woo,Yoon, Young-Woong,Lee, Eu-Ru,Baek, Won-Kyung,Jung, Hyung-Sup 대한원격탐사학회 2022 大韓遠隔探査學會誌 Vol.38 No.6
The rise in temperature induced by global warming caused in El Nino and La Nina, and abnormally changed the temperature of seawater. Rainfall concentrates in some locations due to abnormal variations in seawater temperature, causing frequent abnormal floods. It is important to rapidly detect flooded regions to recover and prevent human and property damage caused by floods. This is possible with synthetic aperture radar. This study aims to generate a model that directly derives flood-damaged areas by using modified U-NET and TerraSAR-X images based on Multi Kernel to reduce the effect of speckle noise through various characteristic map extraction and using two images before and after flooding as input data. To that purpose, two synthetic aperture radar (SAR) images were preprocessed to generate the model's input data, which was then applied to the modified U-NET structure to train the flood detection deep learning model. Through this method, the flood area could be detected at a high level with an average F1 score value of 0.966. This result is expected to contribute to the rapid recovery of flood-stricken areas and the derivation of flood-prevention measures.
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