Yu, Dingfeng, Li, Huiping, Chen, Chen, Xing, Qianguo, Tao, Bangyi, Kong, Xiangfeng and Pan, Shunqi  ORCID: https://orcid.org/0000-0001-8252-5991
2026.
A multi-attention and edge deep supervision neural network for capturing floating Ulva prolifera using GF-1 satellite imagery.
Journal of Oceanology and Limnology
10.1007/s00343-026-5403-2
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Abstract
Frequent outbreaks of Ulva prolifera in China’ s Yellow Sea—characterized by strong seasonality, large scale, and long drift distances—have become a major marine ecological disaster. Satellite remote sensing integrated with deep learning enables rapid and accurate U. prolifera monitoring. However, surface-aggregated U. prolifera exhibits variable morphology and density, which imposes high requirements on the robustness and generalization of deep learning-based segmentation networks. In this study, we propose a multi-attention and edge deep supervision neural network (MA-EDSNN) model based on Gaofen-1 (GF-1) data to achieve high-precision U. prolifera semantic segmentation (especially for sparse floating regions and edge details). The model integrates a self-designed scale-aware channel attention (SACA) module with attention gate (AG) and convolutional block attention module (CBAM) in each decoder block to achieve multi-scale feature fusion. It further constructs a novel Hybrid Edge-supervised Segmentation loss (HES loss) to optimize boundary and small-object segmentation, and incorporates a high-frequency enhancement module (HFEM) and Swin Transformer into the encoder to enhance edge perception and contextual understanding. Applications of the model in the southern Yellow Sea demonstrate that MA-EDSNN outperforms its pre-improvement version (Accuracy=99.08%, mIoU=91.77%, F1=95.4%) in extracting fine-scale U. prolifera patches, sparse U. prolifera, and their edge details; it also outperforms methods like index-threshold method, random forest, and U-Net across multiregional test sets, verifying its generalization and robustness, and providing an efficient solution for marine ecological environment monitoring.
| Item Type: | Article |
|---|---|
| Date Type: | Published Online |
| Status: | In Press |
| Schools: | Schools > Engineering |
| Additional Information: | RRS policy applied |
| Publisher: | Springer Science and Business Media LLC |
| ISSN: | 2096-5508 |
| Date of First Compliant Deposit: | 10 June 2026 |
| Date of Acceptance: | 14 January 2026 |
| Last Modified: | 10 Jun 2026 09:30 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/187454 |
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