Abstract
Retrievals of sea ice drift from synthetic aperture radar (SAR) images at high spatial resolution are valuable for understanding kinematic behavior and deformation processes of the ice at different spatial scales. Ice deformation causes temporal changes in patterns observed in sequences of SAR images; which makes it difficult to retrieve ice displacement with algorithms based on correlation and feature identification techniques. Here, we propose two extensions to a pattern matching algorithm, with the objective to improve the reliability of the retrieved sea ice drift field at spatial resolutions of a few hundred meters. Firstly, we extended a reliability assessment proposed in an earlier study, which is based on analyzing texture and correlation parameters of SAR image pairs, with the aim to reject unreliable pattern matches. The second step is specifically adapted to the presence of deformation features to avoid the erasing of discontinuities in the drift field. We suggest an adapted detection scheme that identifies linear deformation features (LDFs) in the drift vector field, and detects and replaces outliers after considering the presence of such LDFs in their neighborhood. We validate the improvement of our pattern matching algorithm by comparing the automatically retrieved drift to manually derived reference data for three SAR scenes acquired over different sea ice covered regions.
Highlights
The drift of sea ice can be observed from space by synthetic aperture radar (SAR), and quantified using drift detection algorithms
The drift detection algorithm used in our work provides high-resolution sea ice drift retrievals with spacing between drift vectors between 750–2250 m
The present work introduced two extensions to algorithms for sea ice drift detection based on pattern matching
Summary
The drift of sea ice can be observed from space by synthetic aperture radar (SAR), and quantified using drift detection algorithms. If the drift information has to be provided in near real-time, the algorithm has to be fast, which means that only simple retrieval methods can be used. Such methods result in maps of ice drift with a comparatively coarse spatial resolution. The drift detection algorithm used in our work is based on pattern recognition to determine the displacement of recognizable sea ice structures in sequential SAR images [1]. Our algorithm provides reliable drift and deformation information at a spatial resolution of 15 times the pixel size of the used SAR image. We used SAR images with pixel sizes between 50 and 150 m, which means that the spatial resolutions of the drift
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
