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Hybrid 2D–3D convolution and pre-activated residual networks for hyperspectral image classification

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Abstract

The utilization of Convolutional Neural Networks (CNNs) in hyperspectral image (HSI) classification has become commonplace. However, traditional CNNs cannot fully extract the features of HSI and are prone to gradient vanishing when the network layer is deepened. We suggest a 2D–3D hybrid convolution and pre-activated residual networks-based HSI classification (HSIC) approach to tackle these problems. Firstly, the joint spatial–spectral features of HSI are extracted by a two-layer 3D convolution. Secondly, combining the advantages of 2D and 3D convolution to construct a spatial–spectral feature extraction module based on pre-activated residual networks, which can accelerate the convergence speed of the model while enhancing the capability of advanced spatial semantic feature extraction of HSI. Then, multiple residual modules are connected to take advantage of the different forms of features extracted by each convolutional layer, while multi-feature fusion is performed between blocks to achieve feature complementarity. Finally, a long-distance residual connection is introduced to fuse the shallow and deep features effectively, which further strengthens the expression ability of features. The results of the experiments conducted on three HSIs show that the overall classification accuracy of the model reaches 99.56%, 99.45% and 99.43%, respectively, when 10%, 1% and 1% of samples are randomly selected for training in each ground object class. Compared with other related CNN-based HSI classification models, our model can obtain higher classification accuracy. Consequently, the suggested method is capable of achieving feature reuse and obtaining deep high-level spatial–spectral features with superior discriminative and robustness, and its classification performance is superior to that of existing state-of-the-art methods.

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Data availability

The data that support the findings of this study are openly available in http://www.ehu.eus/ccwintco/index.php?title=Hyperspectral_Remote_Sensing_Scenes

References

  1. Zhao, C., Wang, M., Feng, S.: A sparse and spectral smooth regularized low-rank tensor decomposition method for hyperspectral target detection. Int. J. Remote Sens. 43(12), 4608–4629 (2022)

    Article  Google Scholar 

  2. Gao, H., Wang, M., Sun, X., Cao, X., et al.: Unsupervised dimensionality reduction of medical hyperspectral imagery in tensor space. Comput. Methods Progr. Biomed. 240, 107724 (2023)

    Article  Google Scholar 

  3. Liu, G., Wang, L., Liu, D.: Hyperspectral image classification based on a least square bias constraint additional empirical risk minimization nonparallel support vector machine. Remote Sens. 14(17), 4263 (2022)

    Article  Google Scholar 

  4. Wang, H., Celik, T.: Sparse representation-based hyperspectral image classification. Sign. Image Video Process. 12(5), 1009–1017 (2018)

    Article  Google Scholar 

  5. Tan, X., Xue, Z., Yu, X., Sun, Y., et al.: Hyperspectral image classification with deep 3D capsule network and Markov random field. IET Image Process. 16(1), 79–91 (2022)

    Article  Google Scholar 

  6. Yang, L., Chen, J., Zhang, R., Yang, S., et al.: Precise crop classification of UAV hyperspectral imagery using kernel tensor slice sparse coding based classifier. Neurocomputing 551, 126487 (2023)

    Article  Google Scholar 

  7. Hu, W., Huang, Y., Wei, L., Zhang, F., et al.: Deep convolutional neural networks for hyperspectral image classification. J. Sensors 2015, 258619 (2015)

    Article  Google Scholar 

  8. Zhao, W., Du, S.: Learning multiscale and deep representations for classifying remotely sensed imagery. ISPRS J. Photogramm. Remote Sens. 113, 155–165 (2016)

    Article  Google Scholar 

  9. Li, Y., Zhang, H., Shen, Q.: Spectral–spatial classification of hyperspectral imagery with 3D convolutional neural network. Remote Sens. 9(1), 67 (2017)

    Article  Google Scholar 

  10. Zheng, J., Feng, Y., Bai, C., Zhang, J.: Hyperspectral image classification using mixed convolutions and covariance pooling. IEEE Trans. Geosci. Remote Sens. 59(1), 522–534 (2021)

    Article  Google Scholar 

  11. Fırat, H., Asker, M.E., Hanbay, D.: Classification of hyperspectral remote sensing images using different dimension reduction methods with 3D/2D CNN. Remote Sens. Appl.: Soc. Environ. 25, 100694 (2022)

    Google Scholar 

  12. Liu, Z., Mao, X., Huang, J., Gan, M., et al.: Stratified attention dense network for image super-resolution. Sign. Image Video Process. 16(3), 715–722 (2022)

    Article  Google Scholar 

  13. Shi, C., Liao, D., Zhang, T., Wang, L.: Hyperspectral image classification based on 3D coordination attention mechanism network. Remote Sens. 14(3), 608 (2022)

    Article  Google Scholar 

  14. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778; 2016.

  15. Qing, Y., Liu, W.: Hyperspectral image classification based on multi-scale residual network with attention mechanism. Remote Sens. 13(3), 335 (2021)

    Article  Google Scholar 

  16. He, Z., Shi, Q., Liu, K., Cao, J., et al.: Object-oriented mangrove species classification using hyperspectral data and 3-D siamese residual network. IEEE Geosci. Remote Sens. Lett. 17(12), 2150–2154 (2020)

    Article  Google Scholar 

  17. Cao, F., Guo, W.: Deep hybrid dilated residual networks for hyperspectral image classification. Neurocomputing 384, 170–181 (2020)

    Article  Google Scholar 

  18. Dang, L., Pang, P., Lee, J.: Depth-Wise separable convolution neural network with residual connection for hyperspectral image classification. Remote Sens. 12(20), 3408 (2020)

    Article  Google Scholar 

  19. He, S., Jing, H., Xue, H.: Spectral-spatial multiscale residual network for hyperspectral image classification. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 43, 389–395 (2022)

    Article  Google Scholar 

  20. Lei, R., Zhang, C., Zhang, X., Huang, J., et al.: Multiscale feature aggregation capsule neural network for hyperspectral remote sensing image classification. Remote Sens. 14(7), 1652 (2022)

    Article  Google Scholar 

  21. He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part IV 14, pp. 630–645: Springer, 2016

  22. Gao, H., Yang, Y., Yao, D., Li, C.: Hyperspectral image classification with pre-activation residual attention network. IEEE Access 7, 176587–176599 (2019)

    Article  Google Scholar 

  23. Huan, H., Li, P., Zou, N., Wang, C., et al.: End-to-End super-resolution for remote-sensing images using an improved multi-scale residual network. Remote Sens. 13(4), 666 (2021)

    Article  Google Scholar 

  24. Wang, X., Xu, H., Yuan, L., Dai, W., et al.: A remote-sensing scene-image classification method based on deep multiple-instance learning with a residual dense attention convnet. Remote Sens. 14(20), 5095 (2022)

    Article  Google Scholar 

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Funding

This work was supported by Zhejiang Provincial Education Department General Research Project (No. Y202248546), Public Welfare Applied Research Project of Huzhou (No. 2023GZ29), Natural Science Foundation of Huzhou (No. 2023YZ55) and Zhejiang Provincial College Student Innovation and Entrepreneurship Training Program Project (No. S202310347089).

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Authors and Affiliations

  1. School of Information Engineering, Huzhou University, Huzhou, 313000, China

    Huanhuan Lv, Yule Sun, Hui Zhang & Mengping Li

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  1. Huanhuan Lv
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  2. Yule Sun
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  3. Hui Zhang
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  4. Mengping Li
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Contributions

HL conceptualized and designed the algorithm, contributed to algorithm improvements, and critically revised the manuscript for important intellectual content. YS built the model, verified and analyzed it experimentally, prepared the original manuscript draft. HZ assisted with manuscript writing and revisions, supervised the project, provided strategic direction in algorithm development and testing, and conducted a thorough review and final approval of the manuscript prior to submission. ML visualized experimental results. All authors reviewed the manuscript.

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Correspondence to Hui Zhang.

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Lv, H., Sun, Y., Zhang, H. et al. Hybrid 2D–3D convolution and pre-activated residual networks for hyperspectral image classification. SIViP 18, 3815–3827 (2024). https://doi.org/10.1007/s11760-024-03044-0

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