计算机工程与应用2025，Vol.61Issue(10)：258-266,9.DOI:10.3778/j.issn.1002-8331.2401-0300
基于改进DeepLabV3+的轻量化SAR图像冰间水道分割
Lightweight SAR Image Lead Segmentation Based on Improved DeepLabV3+
摘要
Abstract
Sea ice leads are of great significance for polar navigation.In order to obtain accurate and real-time distribu-tion of leads,a lightweight synthetic aperture radar(SAR)image segmentation model based on DeepLabV3+is proposed.MobileNetV3 is employed as the backbone network,and an improved atrous spatial pyramid pooling module using dilated ghost convolution and squeeze-and-excitation channel attention(SE-GASPP)is proposed to achieve encoder lightweight and channel feature enhancement.A local attention module is employed to extract local information from the output features of the backbone network,and a shape preservation module is proposed to generate shape attention to focus on lead shape information and improve feature representation.The model is trained by combining shape loss and segmentation loss.To solve the problem of limited training data for lead segmentation tasks,a dataset is constructed by using SAR dual-polarization data.The experimental results show that the proposed lightweight segmentation model improves the mean intersection over union by 4.92 percentage points,and is superior to other segmentation models,when the parameters are only 7.8%of DeepLabV3+,the floating-point operations are reduced by 70.56%,and the reasoning speed is increased by 10%com-pared to DeepLabV3+.The proposed lightweight segmentation model has a high segmentation performance while achieving lightweight.关键词
合成孔径雷达(SAR)/冰间水道分割/轻量化/局部注意力/形状注意力Key words
synthetic aperture radar(SAR)/lead segmentation/lightweight/local attention/shape attention分类
信息技术与安全科学引用本文复制引用
宋巍,祝敏,石少华,柳彬,贺琪..基于改进DeepLabV3+的轻量化SAR图像冰间水道分割[J].计算机工程与应用,2025,61(10):258-266,9.基金项目
国家自然科学基金面上项目(61900240,42376194). （61900240,42376194）
