海南师范大学学报（自然科学版）2026，Vol.39Issue(2)：223-235,13.DOI:10.12051/j.issn.1674-4942.2026.02.013
基于多特征融合和注意力机制的红树林遥感图像语义分割网络
Semantic Segmentation Network of Mangrove Remote Sensing Image Based on Multi-feature Fusion and Attention Mechanism
摘要
Abstract
Mangrove is an ecologically sensitive area that is of great significance to global ecological balance and biodiversity.Due to the complex terrain,changing lighting conditions and diverse vegetation,the remote sensing image segmentation of mangrove areas faces many challenges.Traditional image segmentation methods have poor accuracy for small areas and edge areas of mangrove remote sensing image segmentation.This paper proposes a mangrove remote sensing image segmentation algorithm based on deep learning:a semantic segmentation network of mangrove remote sensing image combining multi-feature fusion and attention mechanism.Firstly,the U-Net structure was improved by combining the structural characteristics of the Darknet-53 model,and the triple decoding features were utilized to build a triple structure U-Net network(T-Unet),which improved the segmentation accuracy of small areas.Secondly,on the basis of T-Unet,by introducing the SE Net and CBAM attention module,the SET Unet(Squeeze Exception Triple Unet)network structure was constructed,which further improves the segmentation effect of boundary areas.The experimental results show that the T-Unet model has significantly improved the effect compared with the standard U-Net model in the small area segmentation scene,and the recall rate of SET Unet model in the boundary region is significantly better than that of the comparison model.These improvements have significantly improved the accuracy of segmentation,especially in small areas and marginal areas,providing effective support for mangrove conservation and biodiversity research.关键词
多特征融合/注意力机制/图像分割/T-Net/SET-UnetKey words
multi-feature fusion/attention mechanism/image segmentation/T-Net/SET-Unet分类
信息技术与安全科学引用本文复制引用
张安健,侯世闯,吴淑雷,陈焕东..基于多特征融合和注意力机制的红树林遥感图像语义分割网络[J].海南师范大学学报（自然科学版）,2026,39(2):223-235,13.基金项目
国家自然科学基金项目(61966013) （61966013）
