邢孟道,
孙光才
西安电子科技大学雷达信号处理国家重点实验室 西安 710071
西安电子科技大学信息感知技术协同创新中心 ??西安 ??710071
基金项目:国家重点研发计划(2017YFC1405600)，国家自然科学基金创新群体基金(61621005)
详细信息
作者简介:张金松(1995–)，男，山东德州人，西安电子科技大学信号与信息处理专业博士研究生，研究方向为SAR图像解译，深度学习及SAR成像。E-mail: jinsongxd@163.com
邢孟道(1975–)，男，浙江嵊州人。西安电子科技大学教授，博士生导师，主要研究方向为雷达成像、目标识别和天波超视距雷达信号处理。E-mail: xmd@xidian.edu.cn
孙光才(1984–)，男，湖北孝感汉川人。西安电子科技大学副教授，博士生导师，主要研究方向为多通道波束指向 SAR 成像和 SAR 动目标成像。E-mail: rsandsgc@126.com
通讯作者:张金松 jinsongxd@163.com
中图分类号:TN958计量
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被引次数:0
出版历程
收稿日期:2019-01-14
修回日期:2019-04-08
网络出版日期:2019-06-19
A Water Segmentation Algorithm for SAR Image Based on Dense Depthwise Separable Convolution
ZHANG Jinsong,,XING Mengdao,
SUN Guangcai
National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
Collaborative Innovation Center of Information Sensing and Understand, Xidian University, Xi’an 710071, China
Funds:The State Key Research Development Program (2017YFC1405600), The Foundation for Innovative Research Groups of the National Natural Science Foundation of China (61621005)
More Information
Corresponding author:ZHANG Jinsong, jinsongxd@163.com
摘要
摘要:SAR图像的水域分割在舰船目标检测、灾害监测等军事和民用领域具有重要意义。针对传统水域分割算法鲁棒性差、难以准确进行分割等问题，该文首先建立了基于高分三号的SAR图像水域分割数据集，并基于深度学习技术提出了基于密集深度分离卷积的分割网络架构，该网络以SAR图像作为输入，通过密集分离卷积和扩张卷积提取图像高维特征，并构造基于双线性插值的上采样解码模块用于输出分割结果。在水域分割数据集上的实验结果表明，与传统方法相比，该方法不仅在分割准确度上有大幅提高，在算法的鲁棒性和分割速度上也具有部分优势，具备较好的工程实用价值。
关键词:合成孔径雷达/
水域分割/
深度学习/
密集分离卷积/
特征提取
Abstract:Water segmentation of real SAR images is of great significance in military and civilian applications such as ship target detection and disaster monitoring. To solve the issues of poor robustness and inaccurate segmentation of traditional water segmentation algorithms, this paper first establishes a SAR water segmentation dataset based on the GF3 satellite and then presents a segmentation network architecture based on depthwise separable convolution. The network takes real SAR images as inputs, extracts high-dimensional features through depthwise separable and dilated convolutions, constructs an up-sampling and decoding module based on bilinear interpolation, and then outputs the corresponding segmentation results. The segmentation results of a water segmentation dataset show that the proposed segmentation method remarkably improves the segmentation accuracy, the segmentation robustness and running speed than traditional method. Therefore, the findings demonstrate the excellent practical engineering value of the proposed algorithm.
Key words:Synthetic Aperture Radar (SAR)/
Water segmentation/
Deep learning/
Dense separable convolution/
Feature extraction
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