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

Author:

Yang Kun1,Li Haiyan12ORCID,Perrie William3ORCID,Scharien Randall Kenneth4ORCID,Wu Jin15,Zhang Menghao1,Xu Fan1

Affiliation:

1. College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China

2. Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China

3. Fisheries & Oceans Canada, Bedford Institute of Oceanography, Dartmouth, NS B2Y 4A2, Canada

4. Department of Geography, University of Victoria, Victoria, BC V8P 5C2, Canada

5. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

Abstract

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 (σhh0), Shannon entropy (SEi), the spherical scattering component of Krogager decomposition (Ks)), and hybrid polarization parameters (horizontal receive intensity(σrh0), hybrid-pol Shannon entropy (CPSEi), the correlation coefficient (ρrh−rv) between the σrh0 and σrv0, and the surface scattering component of m − α decomposition αs) 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.

Funder

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

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

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