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Automated Sea Ice Classification Using Sentinel-1 Imagery

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Title
Automated Sea Ice Classification Using Sentinel-1 Imagery
Other Titles
Sentinel-1 영상을 이용한 해빙 자동 분류
Authors
Park, Jeong-Won
Korosov, Anton
Babiker, Mohamed
Kim, Hyun-cheol
Issue Date
2019
Citation
Park, Jeong-Won, et al. 2019. Automated Sea Ice Classification Using Sentinel-1 Imagery. IEEE International Geosciences and Remote Sensing Symposium. Pacifico Yokohama. 2019.07.29~2019.08.02.
Abstract
Sentinel-1A and 1B operate in Extra Wide swath dualpolarization mode over the Arctic Seas, and the two-satellite constellation provides the most frequent SAR observation of the Arctic sea ice ever. However, the use of Sentinel-1 for sea ice classification has not been popular because of relatively higher level of system noise and radiometric calibration issues. By taking advantage of our recent development on Sentinel-1 image noise correction, we suggest a fully automated SAR image-based sea ice classification scheme which can provide a potential near-real time services of sea ice charting. The denoised images are processed into texture features and a machine learning-based classifier is trained by feeding digitized ice charts. The use of ice chart rather than manually classified reference image makes enable an automated training which minimizes the effects from biased human decision. The resulting classifier was tested over the Fram Strait area for an extensive dataset of Sentinel-1 constellation acquired from October 2017 to May 2018. The classification results are shown in comparison with the ice charts, and the feasibility of the ice chart-feeded automated classifier is discussed.
URI
https://repository.kopri.re.kr/handle/201206/12638
Conference Name
IEEE International Geosciences and Remote Sensing Symposium
Conference Place
Pacifico Yokohama
Conference Date
2019.07.29~2019.08.02
Appears in Collections  
2019-2019, Research on analytical technique for satellite observation of Arctic sea ice (19-19) / Kim, Hyun-cheol (PE19120)
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