西安电子科技大学学报（自然科学版）2017，Vol.44Issue(1)：100-105,6.DOI:10.3969/j.issn.1001-2400.2017.01.018
一种自动的高分辨率遥感影像道路提取方法
Automatic road extraction method for high-resolution remote sensing images
摘要
Abstract
Road extraction from high‐resolution satellite images is very important .Due to image noise , the natural scene complexity , and the extraction algorithms limitations , it still needs to be further researched . In recent years , level set evolution has been used to extract the road , but it is difficult to automatically generate initial level curves for the level set evolution ( LSE) . In this paper , we propose an automatic approach to the generation of initial level curves and use it to extract the road . Firstly , the convolutional neural network(CNN) is used to classify the road or nonroad , then shape features are adopted to filter nonlinear features to get the accurate road region . And on this basis , we exploit tensor voting to detect the road junctions and utilize them as initial level curves;finally we fuse the results obtained by the CNN and LSE . Experiments show that this algorithm can get an accurate and complete road .关键词
卷积神经网络/形状特征分析/张量投票/水平集分割/信息融合Key words
convolutional neural network(CNN)/shape feature/tensor voting/level set/information fusion分类
信息技术与安全科学引用本文复制引用
刘如意,宋建锋,权义宁,许鹏飞,雪晴,杨云,苗启广..一种自动的高分辨率遥感影像道路提取方法[J].西安电子科技大学学报（自然科学版）,2017,44(1):100-105,6.基金项目
国家自然科学基金资助项目（61472302，61272280，U1404620，41271447）；教育部新世纪优秀人才支持计划资助项目（NCET-12-0919）；中央高校基本科研业务费专项资金资助项目（K5051203020，JB150313，K5051303018，BDY081422）；陕西省自然科学基金资助项目（2014JM8310，2010JM8027）；西安市科技局资助项目（CXY1441（1））；地理信息工程国家重点实验室开放研究基金资助项目 （）
