计算机技术与发展2025，Vol.35Issue(7)：8-15,8.DOI:10.20165/j.cnki.ISSN1673-629X.2025.0039
增强特征提取和解释的遥感图像语义分割模型
A Semantic Segmentation Model for Remote Sensing Images with Enhanced Feature Extraction and Interpretation
摘要
Abstract
DeepLab V3+is a semantic segmentation model with an Encoder-Decoder structure.Due to its characteristic of per-pixel clas-sification,it is suitable for dealing with the land cover classification problem of remote sensing images.However,the loss of feature maps caused by the downsampling process will lead to the appearance of discontinuous unlabeled void areas inside continuous large-scale ground objects,and the bilinear interpolation algorithm will lose the details of segmentation edges.In response to the above problems,we propose a V3plus-EN-TC model improved based on DeepLab V3+.The backbone network is replaced with EfficientNet,which has a stronger feature extraction ability.The SE module and inverted residual connections are introduced to enhance the Encoder's ability to perceive and extract channel information and multi-scale spatial information.Features at three levels are fused,and an upsampling method combining transposed convolution and bilinear interpolation is adopted to improve the feature interpretation ability of the Decoder,suppress the appearance of void areas,and improve the edge accuracy.The DiceFocal combined loss function is utilized to solve the problem of unbalanced sample distribution and further focus on mixed pixels.On the preprocessed remote sensing dataset GID,compared with models such as FCN,U-Net,SegNet,PSPNet,CBAM-DeepLab V3+,and CRF-DeepLab V3+,the improved model V3plus-EN-TC has significantly fewer void areas and improved model accuracy.The mean intersection over union,F1 score,and mean pixel accuracy of the improved model reach 84.74％,88.39％,and 86.64％respectively.关键词
遥感图像/语义分割/DeepLab V3+/EfficientNet/转置卷积/损失函数Key words
remote sensing image/semantic segmentation/DeepLab V3+/EfficientNet/transposed convolution/loss function分类
信息技术与安全科学引用本文复制引用
于攀琳,吴旭,张凌云,刘子涵..增强特征提取和解释的遥感图像语义分割模型[J].计算机技术与发展,2025,35(7):8-15,8.基金项目
四川省科学研究项目(25NSFSC1726) （25NSFSC1726）
