Fine Resolution Classification of New Ice, Young Ice, and First-Year Ice Based on Feature Selection from Gaofen-3 Quad-Polarization SAR
文献类型：期刊论文
| 作者 | Yang, Kun2; Li, Haiyan2,3; Perrie, William4; Scharien, Randall Kenneth5; Wu, Jin1,2; Zhang, Menghao2; Xu, Fan2 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2023-05-04 |
| 卷号 | 15期号:9页码:22 |
| 关键词 | fine sea ice classification random forest classifier polarization SAR Gaofen-3 feature selection |
| DOI | 10.3390/rs15092399 |
| 通讯作者 | Li, Haiyan(lihaiyan@ucas.ac.cn) |
| 英文摘要 | A new method of sea ice classification based on feature selection from Gaofen-3 polarimetric Synthetic Aperture Radar (SAR) observations was proposed. The new approach classifies sea ice into four categories: open water (OW), new ice (NI), young ice (YI), and first-year ice (FYI). Seventy parameters that have previously been applied to sea ice studies were re-examined for sea ice classification in the Okhotsk Sea near the melting point on 28 February 2020. The 'separability index (SI)' was used for the selection of optimal features for sea ice classification. Full polarization parameters (the backscatter intensity contains the horizontal transmit-receive intensity (s(hh)(0)), Shannon entropy (SEi), the spherical scattering component of Krogager decomposition (K-s)), and hybrid polarization parameters (horizontal receive intensity(s(rh)(0)), hybrid-pol Shannon entropy (CPSEi), the correlation coefficient (?(rh-rv)) between the s(rh)(0) and s(rv)(0), and the surface scattering component of m - a decomposition as) were determined as the optimal parameters for the different work modes of SAR. The selected parameters were used to classify sea ice by the random forest classifier (RFC), and classification results were validated by manually interpreted ice maps derived from Landsat-8 data. The classification accuracy of OW, NI, YI and FYI reached 95%, 96%, 98% and 85%, respectively. |
| WOS关键词 | SYNTHETIC-APERTURE RADAR ; SEA-ICE ; SCATTERING MODEL ; POLARIMETRIC PARAMETERS ; TARGET DECOMPOSITION ; BAND SAR ; SENSITIVITY ; SIGNATURES ; ROUGHNESS ; ENTROPY |
| 资助项目 | National Key R&D Program of China[2022YFC3104900] ; National Key R&D Program of China[2022YFC3104904] ; Fundamental Research Funds for the Central Universities ; Canadian Ocean Frontier Institute ; Canadian Space Agency program for SWOT satellite ; Government of Canada Competitive Science Research Fund ; Natural Sciences and Engineering Research Council of Canada Discovery Grants Program[RGPIN-2022-05217] |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| 出版者 | MDPI |
| WOS记录号 | WOS:000988137000001 |
| 资助机构 | National Key R&D Program of China ; Fundamental Research Funds for the Central Universities ; Canadian Ocean Frontier Institute ; Canadian Space Agency program for SWOT satellite ; Government of Canada Competitive Science Research Fund ; Natural Sciences and Engineering Research Council of Canada Discovery Grants Program |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/197337]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Li, Haiyan |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Coll Earth & Planetary Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Oceanol, Qingdao 266071, Peoples R China 4.Fisheries & Oceans Canada, Bedford Inst Oceanog, Dartmouth, NS B2Y 4A2, Canada 5.Univ Victoria, Dept Geog, Victoria, BC V8P 5C2, Canada |
| 推荐引用方式 GB/T 7714 | Yang, Kun,Li, Haiyan,Perrie, William,et al. Fine Resolution Classification of New Ice, Young Ice, and First-Year Ice Based on Feature Selection from Gaofen-3 Quad-Polarization SAR[J]. REMOTE SENSING,2023,15(9):22. |
| APA | Yang, Kun.,Li, Haiyan.,Perrie, William.,Scharien, Randall Kenneth.,Wu, Jin.,...&Xu, Fan.(2023).Fine Resolution Classification of New Ice, Young Ice, and First-Year Ice Based on Feature Selection from Gaofen-3 Quad-Polarization SAR.REMOTE SENSING,15(9),22. |
| MLA | Yang, Kun,et al."Fine Resolution Classification of New Ice, Young Ice, and First-Year Ice Based on Feature Selection from Gaofen-3 Quad-Polarization SAR".REMOTE SENSING 15.9(2023):22. |
入库方式： OAI收割
来源：地理科学与资源研究所
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