A Two-Scale Method of Sea Ice Classification Using TerraSAR-X ScanSAR Data During Early Freeze-Up
Huiying Liu, Huadong Guo, Guang Liu
Abstract
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 (GLCM) features in a specific resolution are used as classification bases. The classification is implemented only once. In this way, however, the scalloping and inter-scan banding (ISB) artifacts in some TerraSAR-X ScanSAR data affect the classification result. In this paper, 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 (SVM) 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 multi-year ice (MYI), first-year ice (FYI), and water. The validation is accomplished using independent images, with an overall average accuracy of 86.38%. This study shows that this method may provide an alternative for sea ice classification using TerraSAR-X ScanSAR data.