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DeepLabV3+ 모델을 이용한 PlanetScope 영상의 해상 유출유 탐지
강종구,윤유정,김근아,박강현,최소연,양찬수,이종혁,이양원,Kang, Jonggu,Youn, Youjeong,Kim, Geunah,Park, Ganghyun,Choi, Soyeon,Yang, Chan-Su,Yi, Jonghyuk,Lee, Yangwon 대한원격탐사학회 2022 大韓遠隔探査學會誌 Vol.38 No.6
Since oil spills can be a significant threat to the marine ecosystem, it is necessary to obtain information on the current contamination status quickly to minimize the damage. Satellite-based detection of marine oil spills has the advantage of spatiotemporal coverage because it can monitor a wide area compared to aircraft. Due to the recent development of computer vision and deep learning, marine oil spill detection can also be facilitated by deep learning. Unlike the existing studies based on Synthetic Aperture Radar (SAR) images, we conducted a deep learning modeling using PlanetScope optical satellite images. The blind test of the DeepLabV3+ model for oil spill detection showed the performance statistics with an accuracy of 0.885, a precision of 0.888, a recall of 0.886, an F1-score of 0.883, and a Mean Intersection over Union (mIOU) of 0.793.