• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学

• 图形与图像 • 上一篇    下一篇

基于ELM的遥感影像城市道路提取

蔡衡1,2,3,楚恒1,2,4,单德明1,2,3   

  1. (1.重庆邮电大学通信与信息工程学院,重庆 400065;2.重庆高校市级光通信与网络重点实验室,重庆 400065;
    3.泛在感知与互联重庆市重点实验室,重庆 400065;4.重庆市勘测院,重庆 400020)
  • 收稿日期:2019-03-12 修回日期:2019-05-30 出版日期:2020-01-25 发布日期:2020-01-25
  • 基金资助:

    重庆高校创新团队建设计划(CXTDX201601020)

ELM-based urban road extraction
from remote sensing images

CAI Heng1,2,3,CHU Heng1,2,4,SHAN De-ming1,2,3   

  1. (1.School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065;
    2.Key Laboratory of Optical Communication and Network in Chongqing,Chongqing 400065;
    3.Key Laboratory of Ubiquitous Sensing and Networking in Chongqing,Chongqing 400065;
    4.Chongqing Survey Institute,Chongqing 400020,China)
     
  • Received:2019-03-12 Revised:2019-05-30 Online:2020-01-25 Published:2020-01-25

摘要:

针对高分辨率遥感影像中复杂场景道路提取不理想问题,利用极限学习机ELM的快速学习能力,提出了一种基于ELM的城市道路提取方法。首先,利用改进的布谷鸟搜索CS算法自适应地选择ELM的隐含层节点数,以提高模型的稳定性;其次,引入数据样本蕴含的判别信息,弥补ELM学习不够充分问题,进而提高ELM分类性能;最后,结合数学形态学处理,对提取道路进行优化,获得最终的道路提取效果。遥感影像道路提取实验结果表明,所提方法不仅增强了网络的稳定性,同时还提高了道路提取的精确度,能较好地提取出道路信息。

关键词: 高分辨率遥感影像, 极限学习机, 布谷鸟搜索, 判别信息, 数学形态学

Abstract:

Aiming at the unsatisfactory road extraction of complex scenes and the fast learning ability of Extreme Learning Machine (ELM) in high-resolution remote sensing images, an ELM-based urban road extraction method is proposed. Firstly, the improved Cuckoo Search algorithm (CS) is used to adaptively select the number of hidden layer nodes of the ELM, in order to improve the stability of the model. Secondly, the discriminant information in the data sample is introduced to make up for the insufficient ELM learning, thus improving the ELM classification performance. Finally, the mathematical morphology processing is used to optimize the extracted road so as to obtain the final road extraction effect. The road extraction test results of remote sensing image show that the proposed method not only enhances the stability of the network, but also improves the accuracy of road extraction, and can extract road information better.
 

Key words: high resolution remote sensing image, Extreme Learning Machine (ELM), Cuckoo Search(CS), discriminant information, mathematical morphology