曲阜师范大学学报（自然科学版）2024，Vol.50Issue(3)：81-88,8.DOI:10.3969/j.issn.1001-5337.2024.3.081
基于U-Net和小波变换的SAR图像道路分割算法
Road segmentation algorithm for SAR images based on U-Net and wavelet transform
摘要
Abstract
Traditional SAR image road segmentation is greatly affected by speckle noise,and has diffi-cult in utilizing high-frequency information in the image,and low segmentation accuracy.In response to the above problems,this paper proposes a SAR image road segmentation algorithm based on the wavelet transform attention mechanism and U-Net.A frequency domain attention mechanism based on wavelet transform is designed;a hybrid pooling mechanism is introduced to enhance the elongated features of roads in SAR images;stripe and pyramid hybrid pooling and frequency domain attention are added to U-Net.On this basis,a convolutional neural network for road segmentation in SAR images is designed.The algorithm in this paper can effectively suppress the noise existing in SAR images,and at the same time suppress irrelevant feature channels,thereby effectively utilizing image features.The frequency domain at-tention mechanism achieves denoising function while retaining the effective information of the image,which enhances the robustness of the algorithm.The hybrid pooling mechanism strengthens the road char-acteristics and improves the segmentation accuracy.Real airborne high-resolution SAR image data were used to conduct road segmentation experiments.The results show that the algorithm in this paper has good segmentation effects and its effectiveness is verified.关键词
SAR图像道路提取/卷积神经网络/小波变换/通道注意力Key words
SAR image road extraction/convolutional neural networks/wavelet transform/channel attention分类
信息技术与安全科学引用本文复制引用
刘伟韬,潘志刚..基于U-Net和小波变换的SAR图像道路分割算法[J].曲阜师范大学学报（自然科学版）,2024,50(3):81-88,8.基金项目
国家重点研发计划(2017YFB0503001). （2017YFB0503001）
