Fine Resolution Classification of New Ice, Young Ice, and First-Year Ice Based on Feature Selection from Gaofen-3 Quad-Polarization SAR

被引:2
作者
Yang, Kun [1 ]
Li, Haiyan [1 ,2 ]
Perrie, William [3 ]
Scharien, Randall Kenneth [4 ]
Wu, Jin [1 ,5 ]
Zhang, Menghao [1 ]
Xu, Fan [1 ]
机构
[1] Univ Chinese Acad Sci, Coll Earth & Planetary Sci, Beijing 100049, Peoples R China
[2] Chinese Acad Sci, Inst Oceanol, Qingdao 266071, Peoples R China
[3] Fisheries & Oceans Canada, Bedford Inst Oceanog, Dartmouth, NS B2Y 4A2, Canada
[4] Univ Victoria, Dept Geog, Victoria, BC V8P 5C2, Canada
[5] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
基金
国家重点研发计划; 加拿大自然科学与工程研究理事会;
关键词
fine sea ice classification; random forest classifier; polarization SAR; Gaofen-3; feature selection; SYNTHETIC-APERTURE RADAR; SEA-ICE; SCATTERING MODEL; POLARIMETRIC PARAMETERS; TARGET DECOMPOSITION; BAND SAR; SENSITIVITY; SIGNATURES; ROUGHNESS; ENTROPY;
D O I
10.3390/rs15092399
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
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.
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页数:22
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