自然资源遥感2025，Vol.37Issue(2)：1-10,10.DOI:10.6046/zrzyyg.2023312
结合上下文与类别感知特征融合的高分遥感图像语义分割
Semantic segmentation of high-resolution remote sensing images based on context-and class-aware feature fusion
摘要
Abstract
To address the accuracy reduction in the semantic segmentation of remote sensing images due to insufficient extraction of contextual dependencies and loss of spatial details,this study proposed a semantic segmentation method based on context-and class-aware feature fusion.With ResNet-50 as the backbone network for feature extraction,the proposed method incorporates the attention module during downsampling to enhance feature representation and contextual dependency extraction.It constructs a large receptive field block on skip connections to extract rich multiscale contextual information,thereby mitigating the impacts of scale variations between targets.Furthermore,it connects a scene feature association and fusion module in parallel behind the block to guide local feature fusion based on global features.Finally,it constructs a class prediction module and a class-aware feature fusion module in the decoder part to accurately fuse the low-level advanced semantic information with high-level detailed information.The proposed method was validated on the Potsdam and Vaihingen datasets and compared with six commonly used methods,including DeepLabv3+and BuildFormer,to verify its effectiveness.Experimental results demonstrate that the proposed method outperformed other methods in terms of recall,F1-score,and accuracy.Particularly,it yielded intersection over union(IoU)values of 90.44％ and 86.74％ for building segmentation,achieving improvements of 1.55％ and 2.41％,respectively,compared to suboptimal networks DeepLabv3+and A2FPN.关键词
类别感知/语义分割/遥感图像/上下文信息/特征融合Key words
class-aware/semantic segmentation/remote sensing image/contextual information/feature fusion分类
计算机与自动化引用本文复制引用
何晓军,罗杰..结合上下文与类别感知特征融合的高分遥感图像语义分割[J].自然资源遥感,2025,37(2):1-10,10.基金项目
辽宁省教育厅科学研究经费项目"基于智能多主体的并行化海量遥感影像分割方法研究"(编号:LJKZ0350)资助. （编号:LJKZ0350）
