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Simulation Tools and Techniques. 13th EAI International Conference, SIMUtools 2021, Virtual Event, November 5-6, 2021, Proceedings

Research Article

Agricultural Hyperspectral Image Classification Based on Deep Separable Convolutional Neural Networks

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  • @INPROCEEDINGS{10.1007/978-3-030-97124-3_30,
        author={Yangyang Liang and Yu Wu and Gengke Wang and Lili Zhang},
        title={Agricultural Hyperspectral Image Classification Based on Deep Separable Convolutional Neural Networks},
        proceedings={Simulation Tools and Techniques. 13th EAI International Conference, SIMUtools 2021, Virtual Event, November 5-6, 2021, Proceedings},
        proceedings_a={SIMUTOOLS},
        year={2022},
        month={3},
        keywords={Residual Network Separable convolution Convolutional neural network Attention mechanism Agricultural hyperspectrum},
        doi={10.1007/978-3-030-97124-3_30}
    }
    
  • Yangyang Liang
    Yu Wu
    Gengke Wang
    Lili Zhang
    Year: 2022
    Agricultural Hyperspectral Image Classification Based on Deep Separable Convolutional Neural Networks
    SIMUTOOLS
    Springer
    DOI: 10.1007/978-3-030-97124-3_30
Yangyang Liang1, Yu Wu2, Gengke Wang2,*, Lili Zhang2
  • 1: Henan University
  • 2: Chinese Academy of Sciences
*Contact email: wanggk@aircas.ac.cn

Abstract

Due to the high computational complexity of traditional convolutional neural networks, the execution time is long and the computational cost is too high. In this paper, we propose a deep separable convolutional neural network with attention mechanism added to improve the classification accuracy and generalization ability of hyperspectral images. The network uses separable convolution combined with residual connections to construct residual units with fewer parameters and adds an attention mechanism layer at the end of the network, which helps to improve the overall performance of the model. So this model has stronger generalization ability now, shorter computation time, and stronger network performance. Finally, the overall accuracy of the model in this paper is 98.48%, 99.1% and 97.40% on the Salinas dataset and the more newly proposed Wuhan Longkou and Wuhan Hanchuan datasets, respectively. It proves that the model has better generalization ability and can complete the calculation in a shorter time. Improving the classification accuracy of hyperspectral images like the Wuhan Longkou dataset is important for agricultural development.

Keywords
Residual Network Separable convolution Convolutional neural network Attention mechanism Agricultural hyperspectrum
Published
2022-03-31
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-97124-3_30
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