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

Jiange Liu; Katharine Andrea Scott; Paul W. Fieguth

doi:10.1117/1.JRS.13.014505

17 January 2019 Detection of marginal ice zone in Synthetic Aperture Radar imagery using curvelet-based features: a case study on the Canadian East Coast

Jiange Liu, Katharine Andrea Scott, Paul W. Fieguth

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 13, Issue 1, 014505 (January 2019). https://doi.org/10.1117/1.JRS.13.014505

Abstract

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.

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Jiange Liu, Katharine Andrea Scott, and Paul W. Fieguth "Detection of marginal ice zone in Synthetic Aperture Radar imagery using curvelet-based features: a case study on the Canadian East Coast," Journal of Applied Remote Sensing 13(1), 014505 (17 January 2019). https://doi.org/10.1117/1.JRS.13.014505

Received: 18 June 2018; Accepted: 19 December 2018; Published: 17 January 2019

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