Abstract
Sea ice drift detection has the key role of global climate analysis and waterway planning. The ability to detect sea ice drift in real-time also contributes to the safe navigation of ships and the prevention of offshore oil platform accidents. In this paper, an Enhanced Delaunay Triangulation (EDT) algorithm for sea ice tracking was proposed for dual-polarization sequential Synthetic Aperture Radar (SAR) images, which was implemented by combining feature tracking with pattern matching based on integrating HH and HV polarization feature information. A sea ice retrieval algorithm for feature detection, matching, fusion, and outlier detection was specifically developed to increase the system’s accuracy and robustness. In comparison with several state-of-the-art sea ice drift retrieval algorithms, including Speeded Up Robust Features (SURF) and the Oriented FAST and Rotated BRIEF (ORB) method, the results of the experiment provided compelling evidence that our algorithm had a higher accuracy than the SURF and ORB method. Furthermore, the results of our method were compared with the drift vector and direction of buoys data. The drift direction is consistent with buoys, and the velocity deviation was about 10 m. It was proved that this method can be applied effectively to the retrieval of sea ice drift.
Highlights
Sea ice drift has an essential influence on the distribution of sea ice on different temporal and spatial scales, and presents a potential risk for navigation and other industrial activities [1]
Fowler et al [19] used a combination of the Scanning Multichannel Microwave Radiometer (SMMR), Sensor Microwave/Imager (SSM/I), Advanced Very High-Resolution Radiometer (AVHRR), and the optical data to first compute the daily movement of Arctic sea ice from sequential satellite images using the maximum-cross correlation (MCC) method, and obtained sea ice motion vectors by merging ice motions from satellite-based infrared and multichannel microwave (MW) images, buoy measurements, and reanalysis data [20,21]
In order to efficiently monitor the sea ice motion, we proposed a novel method with Sentinel-1 Synthetic Aperture Radar (SAR) images for sea ice drift retrieval
Summary
Sea ice drift has an essential influence on the distribution of sea ice on different temporal and spatial scales, and presents a potential risk for navigation and other industrial activities [1]. Fowler et al [19] used a combination of the Scanning Multichannel Microwave Radiometer (SMMR), SSM/I, AVHRR, and the optical data to first compute the daily movement of Arctic sea ice from sequential satellite images using the maximum-cross correlation (MCC) method, and obtained sea ice motion vectors by merging ice motions from satellite-based infrared and multichannel microwave (MW) images, buoy measurements, and reanalysis data [20,21]. These approaches are efforts to overcome the resolution problem of passive microwave measurement. As the amount of SAR imagery is growing, improving the efficiency of the sea ice drift retrieval algorithms is required
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