Combining the U-Net model and a Multi-textRG algorithm for fine SAR ice-water classification

Abstract. Synthetic Aperture Radar (SAR)-based sea ice classification faces challenges due to the similarity among surfaces such as wind-driven open water (OW), smooth thin ice, and melted ice surfaces. Previous algorithms combine pixel-based and region-based machine learning methods or statistical classifiers, yet struggle with hardly improved accuracy arrested by the fuzzy surfaces and limited manual labels. In this study, we propose an automated algorithm framework by combining the semantic segmentation of ice regions and the multi-stage detection of ice pixels to produce high-accuracy and high-resolution ice-water classification data. Firstly, we used the U-Net convolutional neural networks model with the well processed GCOM-W1 AMSR2 36.5 GHz H polarization, Sentinel-1 SAR EW dual-polarization data, and CIS/DMI ice chart labels as data inputs to train and perform semantic segmentation of major ice distribution regions with near-100 % accuracy. Subsequently, within the U-Net semantically segmented ice region, we redesigned the GLCM textures and the HV/HH polarization ratio of Sentinel-1 SAR images to create a combined texture, which served as the basis for the Multi-textRG algorithm to employ multi-stage region growing for retrieving ice pixel details. We validated the SAR classification results on Landsat-8 and Sentinel-2 optical data yielding an overall accuracy (OA) of 84.9 %, a low false negative (FN) of 4.24 % indicating underestimated low backscatter ice surfaces, and a higher false positive (FP) of 10.8 % reflecting their resolution difference along ice edges. Through detailed analyses and discussions of classification results under the similar ice and water conditions mentioned at the beginning, we anticipate that the proposed algorithm framework successfully addresses accurate ice-water classification across all seasons and enhances the labelling process for ice pixel samples.