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Improved convolutional neural network in remote sensing image classification

  • S. I : Intelligent Computing Methodologies in Machine learning for IoT Applications
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Abstract

The classification of land cover is the first step in the analysis and application of remote sensing data in land resources. How to solve the multi-category image recognition and meet certain precision is a key issue in remote sensing image research, which has very important theoretical significance and practical application value. In this study, the algorithm is improved on the basis of convolutional neural network, and experiments are carried out on multi-source remote sensing images with different geomorphologies taken under three different weather conditions to verify the effectiveness and scalability of the improved convolutional neural network. The research results show that the improved algorithm proposed in this paper has certain results in remote sensing image classification and can provide theoretical reference for subsequent related research.

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Acknowledgements

This paper was supported by Major and Special Project of Taizhou Vocational and Technical College.

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  1. Taizhou Vocational & Technical College, Taizhou, 318000, China

    Binghui Xu

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  1. Binghui Xu
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Correspondence to Binghui Xu.

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Xu, B. Improved convolutional neural network in remote sensing image classification. Neural Comput & Applic 33, 8169–8180 (2021). https://doi.org/10.1007/s00521-020-04931-6

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  • DOI: https://doi.org/10.1007/s00521-020-04931-6

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