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Hyperspectral remote sensing image classification based on dense residual three-dimensional convolutional neural network

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Abstract

Data-driven deep learning techniques set the current state of the art in image classification for hyperspectral remote sensing images. The lack of labeled training data and high dimensionality of hyperspectral images, results in these techniques being far from satisfactory with respect to accuracy and efficiency. To address the deficiencies of the existing approaches, we proposed a novel neural network technique, namely, dense residual three-dimensional convolutional neural network (DR-3D-CNN). Tailored for hyperspectral images, this network used 3D convolution instead of the conventional 2D convolution for more effective spectral feature extraction. It also employed dense residual connections to alleviate the problem of gradient dispersion. After the initial classification by the network, the proposed technique further refined the result using multi-label conditional random field optimization. Experimental results on various hyperspectral image datasets showed that the proposed model outperforms existing deep learning techniques with respect to accuracy by a large margin while requiring less training time.

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Acknowledgments

This work was supported by National Natural Science Foundation of China (61705019).

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Authors and Affiliations

  1. Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science & Technology, Nanjing, 210044, China

    Suting Chen & Meng Jin

  2. Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing, 210044, China

    Suting Chen & Jie Ding

Authors
  1. Suting Chen
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  2. Meng Jin
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  3. Jie Ding
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Contributions

Data curation, Suting Chen; Methodology, Meng Jin; Supervision, Suting Chen and Jie Ding; Writing – original draft, Meng Jin; Writing – review & editing, Suting Chen and Meng Jin.

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Correspondence to Suting Chen.

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We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

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Chen, S., Jin, M. & Ding, J. Hyperspectral remote sensing image classification based on dense residual three-dimensional convolutional neural network. Multimed Tools Appl 80, 1859–1882 (2021). https://doi.org/10.1007/s11042-020-09480-7

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