| 刊名 | Agricultural Biotechnology |
| 作者 | Peilong SHI, Shuxin YIN* |
| 作者单位 | College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China |
| DOI | DOI:10.19759/j.cnki.2164-4993.2025.05.008 |
| 年份 | 2025 |
| 刊期 | 5 |
| 页码 | 38-41 |
| 关键词 | Semantic segmentation; Remote sensing images; CNN; Mamba |
| 摘要 | A novel CNN-Mamba hybrid architecture was proposed to address intra-class variance and inter-class similarity in remote sensing imagery. The framework integrates: (1) parallel CNN and visual state space (VSS) encoders, (2) multi-scale cross-attention feature fusion, and (3) a boundary-constrained decoder. This design overcomes CNN's limited receptive fields and ViT's quadratic complexity while efficiently capturing both local features and global dependencies. Evaluations on LoveDA and ISPRS Vaihingen datasets demonstrate superior segmentation accuracy and boundary preservation compared to existing approaches, with the dual-branch structure maintaining computational efficiency throughout the process. |
