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Remote Sensing Image Recognition Using Deep Belief Network

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Mobile Wireless Middleware, Operating Systems and Applications (MOBILWARE 2020)

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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.

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Acknowledgment

This work was supported by the Education and teaching research project of Jingchu university of technology (No. JX2019-032).

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Authors and Affiliations

  1. School of Computer Engineering, Jingchu University of Technology, Jingmen, 448000, Hubei, China

    Min Li

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  1. Min Li
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Correspondence to Min Li .

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Editors and Affiliations

  1. Inner Mongolia University, Hohhot, China

    Wuyungerile Li

  2. Inner Mongolia University, Hohhot, China

    Dalai Tang

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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Li, M. (2020). Remote Sensing Image Recognition Using Deep Belief Network. In: Li, W., Tang, D. (eds) Mobile Wireless Middleware, Operating Systems and Applications. MOBILWARE 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 331. Springer, Cham. https://doi.org/10.1007/978-3-030-62205-3_18

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  • DOI: https://doi.org/10.1007/978-3-030-62205-3_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62204-6

  • Online ISBN: 978-3-030-62205-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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