无线电工程2025，Vol.55Issue(3)：548-557,10.DOI:10.3969/j.issn.1003-3106.2025.03.011
基于ACSBL-DeepLabV3+的遥感图像地物分类方法研究
Research on Remote Sensing Image Land Cover Classification Based on ACSBL-DeepLabV3+
摘要
Abstract
In high-resolution remote sensing image segmentation,DeepLabV3+network has many problems such as large parameters,high training cost,and inaccurate extraction of remote sensing images in complex environments,and small objects are easy to be neglected,which leads to incomplete category extraction and fuzzy boundaries.To address the above problems,an enhanced method based on DeepLabV3+model is proposed for remote sensing image semantic segmentation.In the encoder,the lightweight network MobileNetV2 is used as the backbone feature extraction network,and an Asymmetric Spatial Pyramid Pooling Module(ACS-ASPP)is incorporated.In decoder refinement,the shallow features extracted by the backbone network are continuously added for feature fusion,and Large Selective Kernel Attention(LSK)mechanism is introduced.Experiments on two high-resolution remote sensing image datasets,Vaihingen and Potsdam,demonstrate that the proposed method is superior to several semantic segmentation networks such as U-Net,PSP-Net and Fully Convolutional Network(FCN)in several performance evaluation indexes.The overall mIoU reaches 69.13%and 75.68%,and F1-Score reaches 80.75%and 85.84%,respectively.The experimental results show that the network can effectively classify various categories of objects and has high practical value.关键词
遥感图像多分类/语义分割/DeepLabV3+/选择性大核注意力机制/解码器细化Key words
remote sensing image multi-classification/semantic segmentation/DeepLabV3+/LSK mechanism/decoder refinement分类
信息技术与安全科学引用本文复制引用
冯丹亭,于淼,于晓鹏..基于ACSBL-DeepLabV3+的遥感图像地物分类方法研究[J].无线电工程,2025,55(3):548-557,10.基金项目
吉林省科技发展计划项目(YDZJ202301ZYTS285)Project of Jilin Provincial Science and Technology De-velopment Plan(YDZJ202301ZYTS285) （YDZJ202301ZYTS285）
