Item type |
学術雑誌論文 / Journal Article(1) |
公開日 |
2021-12-24 |
タイトル |
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タイトル |
Application of textural analysis to map the sea ice concentration with sentinel 1A in the western region of the Antarctic Peninsula |
タイトル |
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言語 |
en |
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タイトル |
Application of textural analysis to map the sea ice concentration with sentinel 1A in the western region of the Antarctic Peninsula |
言語 |
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言語 |
eng |
キーワード |
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言語 |
en |
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主題Scheme |
Other |
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主題 |
Textural feature |
キーワード |
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言語 |
en |
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主題Scheme |
Other |
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主題 |
Synthetic aperture radar |
キーワード |
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言語 |
en |
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主題Scheme |
Other |
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主題 |
Sea ice classification |
キーワード |
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言語 |
en |
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主題Scheme |
Other |
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主題 |
Sea ice extent |
資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
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資源タイプ |
journal article |
アクセス権 |
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アクセス権 |
metadata only access |
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アクセス権URI |
http://purl.org/coar/access_right/c_14cb |
著者 |
Hillebrand, Fernando Luis
Barreto, Ikaro Daniel de Carvalho
Bremer, Ulisses Franz
Arigony-Neto, Jorge
Mendes Júnior, Cláudio Wilson
Simões, Jefferson Cardia
Rosa, Cristiano Niederauer da
Jesus, Janisson Batista de
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抄録 |
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内容記述タイプ |
Abstract |
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内容記述 |
This article proposes the mapping of the concentration of sea ice in the oceanic region of the western Antarctic Peninsula using backscatter coefficients and textural features from synthetic aperture radar (SAR) C-band, horizontal single polarization (HH) images obtained by the Sentinel 1 A satellite sensor. Pure samples of open water and sea ice were obtained from SAR images with the aid of PlanetScope optical images during the austral winter. The statistical technique of logistic regression was applied to identify the best textural features to discriminate the two sampled targets. These selected textural features were used with the backscatter coefficients of SAR images in a maximum likelihood supervised classifier to analyze the images. With sea ice classified in the time series between 2016 and 2019 during winter and austral spring, the concentration of sea ice in the study region was calculated and mapped. The textural features statistically indicated by the logistic regression were gray level co-occurrence matrix (GLCM) Mean and GLCM Variance, resulting in the supervised classification having an accuracy of 86% and precision ranging from 86% to 98% in the validation stage. |
書誌情報 |
en : Polar Science
巻 29,
p. 100719,
発行日 2021-09
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ISSN |
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収録物識別子タイプ |
ISSN |
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収録物識別子 |
18739652 |
DOI |
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識別子タイプ |
DOI |
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関連識別子 |
https://doi.org/10.1016/j.polar.2021.100719 |
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関連名称 |
10.1016/j.polar.2021.100719 |