基于Multi-Path RefineNet的多特征高分辨率SAR图像道路提取算法  

Road Extraction Algorithm of Multi-feature High-resolution SAR Image Based on Multi-Path

作　　者：陈立福[1] 刘燕芝 张鹏 袁志辉 邢学敏[2] CHEN Li-fu;LIU Yan-zhi;ZHANG Peng;YUAN Zhi-hui;XING Xue-min(College of Electrical and Information Engineering,Changsha University of Science&Technology,Changsha 410114,China;College of Traffic and Transportation Engineering,Changsha University of Science&Technology,Changsha 410114,China)

机构地区：[1]长沙理工大学电气与信息工程学院,长沙410114 [2]长沙理工大学交通运输工程学院,长沙410114

出　　处：《计算机科学》2020年第3期156-161,共6页Computer Science

基　　金：国家自然科学基金青年科学基金(61701047,41701536);湖南省教育厅优秀青年项目(16B004);湖南省研究生科研创新项目(CX2017B479)~~

摘　　要：为解决现有高分辨率SAR图像道路提取算法自动化较差、普适性不高的问题,提出了一种基于多路径优化网络的多特征提取算法。首先,对SAR图像进行Gabor变换及灰度梯度共生矩阵变换,获取丰富的道路特征信息,联结级联优化网络和残差网络形成多路径优化网络;然后,对SAR原图、获取的低级特征图和标签图进行训练,充分利用每层网络提取的道路特征获取初始分割的道路结果;最后,利用数学形态学运算连接初始道路断裂处并去除虚警。利用所提算法对不同分辨率的SAR图像进行道路提取,实验结果表明,该算法在提取SAR图像道路方面适用范围广且道路提取效果佳。In order to solve the problems of existing SAR image road extraction algorithm with poor automation and poor universality,a multi-feature road extraction algorithm was proposed based on the multi-path refinement network.Firstly,gabor transformation and gray level-gradient co-occurrence matrix transformation are performed on SAR images to obtain rich road feature information.A multi-path refinement network is formed by coupling the cascade refinement network and the residual network.Then,the SAR original image,the acquired low-level feature image and the label image are input into the new network for trai-ning,and the road features extracted from each layer of network are fully utilized to obtain the initial road segmentation results.Finally,mathematical morphology operation is used to connect the initial road fracture and remove false alarm.This algorithm is used for road extraction of SAR images with different resolutions.The experimental results show that this algorithm has a wide range of application in SAR image extraction and the effect of road extraction is better.

关 键 词：合成孔径雷达 道路提取 深度学习 特征提取 数学形态学运算 

分 类 号：TP753[自动化与计算机技术—检测技术与自动化装置]