自然资源遥感2025，Vol.37Issue(5)：62-72,11.DOI:10.6046/zrzyyg.2024286
一种融合上下文语义信息与边缘特征的海陆分割方法
A sea-land segmentation method combining contextual semantic information and edge features
文甜甜 1普运伟 2赵文翔1
作者信息
- 1. 昆明理工大学国土资源工程学院,昆明 650093
- 2. 昆明理工大学国土资源工程学院,昆明 650093||昆明理工大学信息工程与自动化学院,昆明 650500
- 折叠
摘要
Abstract
In optical remote sensing images with complex scenes and rich land cover information,the sea-land segmentation faces challenges such as low positioning accuracy and blurred edges.Therefore,this paper proposed a deep convolutional network model and a sea-land segmentation method that integrate contextual semantic information and edge features.First,the rich target semantic information was extracted from remote sensing images using the FusionNet semantic segmentation network module.Then,multi-scale and hierarchical contextual semantic features were extracted from the segmentation network using the enhanced atrous spatial pyramid pooling(ASPP)module and contextual attention module.Additionally,an edge extraction sub-network was built to extract multi-scale edge features.Finally,the semantic features and edge features were combined through a fusion module,thereby achieving accurate sea-land segmentation.This method was tested with two typical representative datasets.The results showed that this method achieved an overall prediction accuracy of 98.21%,an F1 score of 97.64%,and a boundary F1 score of 89.36%,all significantly outperforming other models.Particularly in complex backgrounds,this method can effectively improve the accuracy of segmentation and edge detection,demonstrating definite advantages in the segmentation of artificial coastlines and ports.关键词
海陆分割/边缘提取/语义分割/多任务学习/上下文注意力模块Key words
sea-land segmentation/edge extraction/semantic segmentation/multi-task learning/contextual atten-tion module分类
计算机与自动化引用本文复制引用
文甜甜,普运伟,赵文翔..一种融合上下文语义信息与边缘特征的海陆分割方法[J].自然资源遥感,2025,37(5):62-72,11.
