计算机技术与发展2025，Vol.35Issue(3)：40-48,9.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0325
基于递归特征与边缘解耦的遥感图像语义分割
Semantic Segmentation of Remote Sensing Images Based on Recursive Features and Edge Decoupling
摘要
Abstract
Semantic segmentation of images is a crucial technology for intelligent interpretation of remote sensing images.However,traditional semantic segmentation methods still face limitations in segmenting weak feature targets,especially when dealing with complex remote sensing images where the edges of targets are often mixed,resulting in relatively coarse handling of edge details.Therefore,we propose a remote sensing image semantic segmentation model based on recursive features and edge decoupling.Firstly,according to the ideas of feature reuse and cross-layer connections,we design a hierarchical recursive feature network in an encoder-decoder structure,aiming to enhance the extraction capability of weak features.Secondly,by combining multi-scale fusion prediction and edge decoupling,the model merges low-and high-level feature maps,deepening the processing of details.It introduces edge supervision by establishing a connection between the target body and the edge,achieving refined handling of edge details.Finally,we conduct ablation and comparative experiments on the Vaihingen and Potsdam datasets provided by ISPRS.The experimental results demonstrate that the proposed semantic segmentation model effectively maintains the internal consistency of targets and achieves refined processing of edge details in segmentation,which significantly improves the accuracy of remote sensing image semantic segmentation.关键词
语义分割/遥感图像/递归特征/多尺度特征融合/边缘解耦结构Key words
semantic segmentation/remote sensing image/recursive features/multi scale feature fusion/edge decoupling structure分类
信息技术与安全科学引用本文复制引用
王学伟..基于递归特征与边缘解耦的遥感图像语义分割[J].计算机技术与发展,2025,35(3):40-48,9.基金项目
吉林省科技发展计划项目(20230203177SF) （20230203177SF）
