A Two-Scale Method of Sea Ice Classification Using TerraSAR-X ScanSAR Data During Early Freeze-Up

• Published in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Main Authors: Huiying Liu, Huadong Guo, Guang Liu
• Format: Article in Journal/Newspaper
• Language: English
• Published: IEEE 2021
• Subjects: Classification; sea ice; synthetic aperture radar (SAR); Ocean engineering; TC1501-1800; Geophysics. Cosmic physics; QC801-809; Sea ice
• Online Access: https://doi.org/10.1109/JSTARS.2021.3122546; https://doaj.org/article/390a37dd990a4aa59bb2b0c6eecefb22

Sea ice classification using synthetic aperture radar (SAR) in the wide-swath mode is one method for ice type retrieval. The conventional method of sea ice classification involves single-scale classification, wherein the backscatter and gray-level co-occurrence matrix features in a specific resolution are used as classification bases. The classification is implemented only once. In this way, however, the scalloping and interscan banding (ISB) artifacts in some TerraSAR-X ScanSAR data affect the classification result. In this article, a two-scale method of sea ice classification is proposed. The two-scale process includes both the coarse- and fine-resolution classifications. First, the coarse-resolution classification utilizes downscale images to generate the ice zone. Subsequently, the fine-scale classification utilizes the full-resolution SAR image and the ice zone to generate the final classification result. The support vector machine is used as the classifier. This two-scale method provides a better classification result compared to the single-scale method in two aspects. First, it effectively avoids the appearance of scalloping and ISB artifacts in the results. Second, it is less time-consuming and provides satisfactory results for discriminating multiyear ice, first-year ice, and water. The validation is accomplished using independent images, with an overall average accuracy of 86.38%. This article shows that this method may provide an alternative for sea ice classification using TerraSAR-X ScanSAR data.