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Mobile Wireless Middleware, Operating Systems and Applications. 9th EAI International Conference, MOBILWARE 2020, Hohhot, China, July 11, 2020, Proceedings

Research Article

Remote Sensing Image Recognition Using Deep Belief Network

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  • @INPROCEEDINGS{10.1007/978-3-030-62205-3_18,
        author={Min Li},
        title={Remote Sensing Image Recognition Using Deep Belief Network},
        proceedings={Mobile Wireless Middleware, Operating Systems and Applications. 9th EAI International Conference, MOBILWARE 2020, Hohhot, China, July 11, 2020, Proceedings},
        proceedings_a={MOBILWARE},
        year={2020},
        month={11},
        keywords={Remote sensing image recognition DBNs CNNs},
        doi={10.1007/978-3-030-62205-3_18}
    }
    
  • Min Li
    Year: 2020
    Remote Sensing Image Recognition Using Deep Belief Network
    MOBILWARE
    Springer
    DOI: 10.1007/978-3-030-62205-3_18
Min Li1,*
  • 1: School of Computer Engineering, Jingchu University of Technology, Jingmen
*Contact email: 49035084@qq.com

Abstract

How to acquire high-dimensional data such as remote sensing image efficiently and accurately has become a research hotpot recent years. Deep learning is a kind of learning method which uses many kinds of simple layers to learn the mapping relation of complex layers. The authors will attempt to apply the deep belief network model (DBN), which is important in deep learning, to remote sensing image recognition. Using the new large-scale remote sensing image data set with abundant changes as the research object, the hierarchical training mechanism of DBNs is studied and compared with CNNS, the results show that the accuracy and speed of DBNs is better than that of CNNS, and more effective information can be obtained.

Keywords
Remote sensing image recognition DBNs CNNs
Published
2020-11-05
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-62205-3_18
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