CREVNet: a Transformer and CNN-based network for accurate segmentation of ice shelf crevasses

The segmentation of crevasses in remote sensing images plays a pivotal role in diverse domains, including crevasse change monitoring, analysis of ice shelf surface water systems, and investigations into ice shelf stability. In response to the limitations in existing crevasses segmentation methods, which struggle to concurrently capture global structures while preserving local details, this letter introduces CREVNet. CREVNet is designed to achieve precise crevasse segmentation, comprising two integral components: the Transformer Path for enhanced local and global feature extraction, and the Convolutional Path for detailed depiction of crevasses. Evaluation on crevasses dataset, created through the integration of optical remote sensing imagery and laser altimetry data, reveals impressive results. CREVNet achieves F1-score, MIoU, and OA values of 80.40%, 80.98%, and 95.24%, respectively. Notably, CREVNet surpasses the performance of prominent deep learning methods, including Unet, DeepLabV3Plus, DFANet, FPN, MobileViT, and TransUnet. These outcomes underscore CREVNet's practical potential for effective crevasses segmentation.