Detection of marginal ice zone in Synthetic Aperture Radar imagery using curvelet-based features: a case study on the Canadian East Coast

被引：9

作者：

Liu, Jiange ^{[1
]}

Scott, Katharine Andrea ^{[2
]}

Fieguth, Paul W. ^{[2
]}

机构：

[1] Northwestern Polytech Univ, Dept Automat, Xian, Shaanxi, Peoples R China

[2] Univ Waterloo, Syst Design Engn, Waterloo, ON, Canada

来源：

JOURNAL OF APPLIED REMOTE SENSING | 2019年 / 13卷 / 01期

关键词：

marginal ice zone; Synthetic Aperture Radar imagery; curvelet transform; curvelet co-occurrence; support vector machine; SEA; CLASSIFICATION; TRANSFORM;

D O I：

10.1117/1.JRS.13.014505

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

Monitoring the marginal ice zone (MIZ) is becoming increasingly important due to recent evidence that the width of the MIZ is changing with climate. A method to automatically detect the MIZ in synthetic aperture radar (SAR) imagery is proposed. The method utilizes the curve-like features of MIZ in SAR images. A multiscale strategy, the curvelet transform, is chosen to extract features from the SAR images. The statistical and co-occurrence features of curvelet coefficients at an appropriate scale are used to identify the MIZ from open water and consolidated ice. Experimental results show a significant increase in classification accuracy (89.7%) compared with the most commonly used MIZ definition from passive microwave sea ice concentration (74%), especially in the diffuse MIZ. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)

引用

页数：14

共 24 条

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