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基于改进DeepLabv3+的遥感图像语义分割算法

宋熙睿 葛洪伟 李婷

测试科学与仪器2025,Vol.16Issue(2):205-215,11.
测试科学与仪器2025,Vol.16Issue(2):205-215,11.DOI:10.62756/jmsi.1674-8042.2025020

基于改进DeepLabv3+的遥感图像语义分割算法

Remote sensing image semantic segmentation algorithm based on improved DeepLabv3+

宋熙睿 1葛洪伟 1李婷1

作者信息

  • 1. 江南大学 康养智能化技术教育部工程研究中心,江苏 无锡 214122||江南大学 人工智能与计算机学院,江苏 无锡 214122
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摘要

Abstract

The convolutional neural network(CNN)method based on DeepLabv3+has some problems in the semantic segmentation task of high-resolution remote sensing images,such as fixed receiving field size of feature extraction,lack of semantic information,high decoder magnification,and insufficient detail retention ability.A hierarchical feature fusion network(HFFNet)was proposed.Firstly,a combination of transformer and CNN architectures was employed for feature extraction from images of varying resolutions.The extracted features were processed independently.Subsequently,the features from the transformer and CNN were fused under the guidance of features from different sources.This fusion process assisted in restoring information more comprehensively during the decoding stage.Furthermore,a spatial channel attention module was designed in the final stage of decoding to refine features and reduce the semantic gap between shallow CNN features and deep decoder features.The experimental results showed that HFFNet had superior performance on UAVid,LoveDA,Potsdam,and Vaihingen datasets,and its cross-linking index was better than DeepLabv3+and other competing methods,showing strong generalization ability.

关键词

语义分割/高分辨率遥感图像/深度学习/Transformer模型/注意力机制/特征融合/编码器/解码器

Key words

semantic segmentation/high-resolution remote sensing image/deep learning/transformer model/attention mechanism/feature fusion/encoder/decoder

引用本文复制引用

宋熙睿,葛洪伟,李婷..基于改进DeepLabv3+的遥感图像语义分割算法[J].测试科学与仪器,2025,16(2):205-215,11.

基金项目

This work was supported by National Natural Science Foundation of China(No.52374155),and Anhui Provincial Natural Science Foundation(No.2308085MF218). (No.52374155)

测试科学与仪器

1674-8042

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