[1]魏 清,艾玲梅,叶雪娜.一种高分辨率遥感图像道路自动提取方法[J].计算机技术与发展,2019,29(06):130-133.[doi:10. 3969 / j. issn. 1673-629X. 2019. 06. 027]
　WEI Qing,AI Ling-mei,YE Xue-na.An Automatic Road Extraction Method for High-resolution Remote Sensing Images[J].,2019,29(06):130-133.[doi:10. 3969 / j. issn. 1673-629X. 2019. 06. 027]
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一种高分辨率遥感图像道路自动提取方法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年06期
页码:
130-133
栏目:
应用开发研究
出版日期:
2019-06-10

文章信息/Info

Title:
An Automatic Road Extraction Method for High-resolution Remote Sensing Images
文章编号:
1673-629X(2019)06-0130-04
作者:
魏 清1 ; 艾玲梅2 ; 叶雪娜2
1. 陕西省交通规划设计研究院,陕西 西安 710065; 2. 陕西师范大学 计算机科学学院,陕西 西安 710119
Author(s):
WEI Qing1 ; AI Ling-mei2 ; YE Xue-na2
1. Shaanxi Provincial Transport Planning Design and Research Institute,Xi’an 710065,China;2. School of Computer Science,Shaanxi Normal University,Xi’an 710119,China
关键词:
道路提取; 卷积神经网络; 批量梯度下降算法; 形状特征分析
Keywords:
road extraction; convolution neural network; mini-batch gradient descent; shape feature analysis
分类号:
TP301
DOI:
10. 3969 / j. issn. 1673-629X. 2019. 06. 027
摘要:
高分辨率遥感图像道路提取是遥感界关注的重要研究领域,如何快速、有效、智能地进行遥感信息分析和处理是当今遥感界迫切需要解决的问题,自动道路提取方法称为道路提取研究的热点。 针对道路提取受遥感图像噪声和树木阴影等复杂自然场景因素影响的问题,提出一种基于卷积神经网络(CNN)和数学形态学算法相结合的自动道路提取方法。首先,构建出深度卷积神经网络模型,在训练卷积神经网络模型时,通过改进的批量随机梯度下降算法(MBGD)来训练深度卷积神经网络;然后,利用训练好的深度卷积神经网络进行道路特征的提取;最后,结合数学形态学优化算法进一步优化道路提取结果。 实验结果表明,该方法能提取出完整的道路区域。
Abstract:
Road extraction from high resolution remote sensing images is an important research field of remote sensing community. How to analyze and process remote sensing information quickly,effectively and intelligently is an urgent problem to be solved in the field of remote sensing. Automatic road extraction is the hot spot in the research of road extraction. Aiming at the problem that road extraction is affected by complex natural scene factors such as remote sensing image noise and tree shadow,we propose an automatic road extraction method based on convolution neural network (CNN) and mathematical morphology algorithm. Firstly,the deep convolution neural network is constructed and trained by the improved MBGD algorithm when training convolution neural network model. Then,the trained deep convolution neural network is used to extract the road features. Finally,the morphological optimization algorithm is used to further optimize the road extraction results. Experiment shows that this method can extract complete road.

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更新日期/Last Update: 2019-06-10