Efficient Sea Ice Classification Built on Few-Shot Learning Framework

Deep learning (DL) methods have become a key technique for automatic sea ice type mapping from synthetic aperture radar (SAR) imagery, yet their deployment in operational sea ice charting is still hindered by scarce labelled data, limited adaptive feature extraction, and the lack of interactive mechanisms, which restrict model generalization, accuracy, and usability, especially in hard-to-classify scenes. To address these bottlenecks, we propose an efficient sea ice classification model, ESICM, targeting four ice types: open water (OW), young ice (YI), first-year ice (FYI), and multiyear ice (MYI), and enhance performance and practicality under label-scarce conditions through three key designs. First, we introduce a few-shot learning (FSL) framework to more effectively exploit limited labels and reduce the reliance of traditional supervised learning on large labelled datasets. Second, inspired by classical sea ice parameter retrieval algorithms, we design a lightweight channel multiply–divide convolution module (CMDM) that strengthens adaptive feature extraction with only ~190k parameters, thereby improving discrimination of multi-scale textures and sea ice types with subtle backscattering differences. Third, we incorporate an interactive mechanism based on the Segment Anything Model (SAM) and couple it with the FSL framework, allowing the classifier to be adjusted with minimal human intervention and thus improving operability in difficult SAR scenes. ESICM is trained on 512 scenes from the AI4Arctic sea ice challenge dataset and evaluated on 20 independent test scenes, achieving 91.73% overall accuracy (OA), 91.29% F1 score, 85.61% Cohen’s kappa, and 71.52% mean intersection over union (mIoU), outperforming comparative DL models by at least 1.35, 1.90, 2.54, and 2.53 percentage points on these metrics, respectively. In melting season scenes, particularly those dominated by MYI, ESICM’s F1 and IoU outperform the second-best model by 22.21% and 19.15%, respectively. Further cross-domain experiments demonstrate that, even when trained on only about one quarter of local scenes, ESICM still achieves the highest accuracy, demonstrating strong cross-regional generalization. Meanwhile, its interactive functionality enables users to refine classification results via prompts in hard-to-classify scenes, substantially enhancing classification performance. Overall, ESICM provides a lightweight, high-accuracy, and interactively adjustable DL solution for SAR-based sea ice classification under limited labelled data, offering robust technical support for polar navigation safety and sea ice environmental monitoring.