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Hyperspectral classification method based on M-ResHSDC

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Abstract

The hyperspectral image (HSI) classification method based on a three-dimensional convolutional neural network (3DCNN) has problems with overfitting in the training process, and difficulty in interacting with long-distance features, which decreases the classification accuracy. This paper proposes a hyperspectral classification method based on a mix-up algorithm and residual hybrid spectral dilated convolution (M-ResHSDC) to deal with the above problems. Firstly, the mix-up algorithm is utilized for increasing the original dataset to a larger number to alleviate the overfitting problem. Secondly, the hybrid spectral dilated convolution (HSDC) is built up through the combination of the spectral dilated convolution operators with different dilated rates. Each branch of HSDC has a different dilated rate, facilitating the extraction of spectral-spatial features at multiple scales. The ability to capture multiple scales could significantly enhance classification accuracy. On this basis, residual hybrid spectral dilated convolution (ResHSDC) is proposed by replacing the convolution layer of the residual block with HSDC. Thirdly, three ResHSDCs are introduced to replace the three convolution layers of 3DCNN respectively to solve the difficult interaction of long-distance features. Finally, the experiments are conducted on three HSI datasets (Indian Pines, University of Pavia, and Salinas). The results show a higher accuracy of 98.87%, 99.32%, and 99.47% respectively than the traditional methods.

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Data availability

Publicly available datasets were analyzed in this study. This data can be found here: https://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes.

References

  1. Bioucas-Dias JM et al (2013) Hyperspectral remote sensing data analysis and future challenges. IEEE Geosci Remote Sens Mag 1(2):6–36

    Article  Google Scholar 

  2. Kemker R, Kanan C (2017) Self-Taught Feature Learning for Hyperspectral Image Classification. IEEE Trans Geosci Remote Sens 55(5):2693–2705

    Article  Google Scholar 

  3. Lu B et al (2020) Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture. Remote Sens 12(16):2659

    Article  MathSciNet  Google Scholar 

  4. Rao TCM et al (2012) A Hierarchical Hybrid SVM Method for Classification of Remotely Sensed Data. J Indian Soc Remote Sens 40:191–200

    Article  Google Scholar 

  5. Liu G, Wang L, Liu D, Fei L, Yang J (2022) Hyperspectral Image Classification Based on Non-Parallel Support Vector Machine. Remote Sensing 14(10):2447

    Article  Google Scholar 

  6. Bo C, Lu H, Wang D (2018) Spectral-spatial K-Nearest Neighbor approach for hyperspectral image classification. Multimedia Tools Appl 77:10419–10436

    Article  Google Scholar 

  7. Song W, Li S, Kang X, Huang K (2016) Hyperspectral image classification based on KNN sparse representation. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp 2411–2414. https://doi.org/10.1109/IGARSS.2016.7729622

  8. Guo Y, Cao H, Han S et al (2018) Spectral–spatial hyperspectralimage classification with k-nearest neighbor and guided filter. IEEE Access 6:18582–18591

    Article  Google Scholar 

  9. Khodadadzadeh M, Li J, Plaza A, Bioucas-Dias JM (2014) A Subspace-Based Multinomial Logistic Regression for Hyperspectral Image Classification. IEEE Geosci Remote Sens Lett 11(12):2105–2109

    Article  Google Scholar 

  10. Chen Y, Lin Z, Zhao X, Wang G, Gu Y (2014) Deep Learning-Based Classification of Hyperspectral Data. IEEE J Select Top Appl Earth Obs Remote Sens 7(6):2094–2107

    Article  Google Scholar 

  11. Chen Y, Zhao X, Jia X (2015) Spectral-Spatial Classification of Hyperspectral Data Based on Deep Belief Network. IEEE J Select Top Appl Earth Obs Remote Sens 8(6):2381–2392

    Article  Google Scholar 

  12. Makantasis K, Karantzalos K, Doulamis A, Doulamis N (2015) Deep supervised learning for hyperspectral data classification through convolutional neural networks. 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp 4959–4962. https://doi.org/10.1109/IGARSS.2015.7326945

  13. Li X, Ding M, Pižurica A (2020) Deep Feature Fusion via Two-Stream Convolutional Neural Network for Hyperspectral Image Classification. IEEE Trans Geosci Remote Sens 58(4):2615–2629

    Article  Google Scholar 

  14. Chen Y, Jiang H, Li C, Jia X, Ghamisi P (2016) Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks. IEEE Trans Geosci Remote Sens 54(10):6232–6251

    Article  Google Scholar 

  15. Wu P, Cui Z, Gan Z, Liu F (2020) Residual Group Channel and Space Attention Network for Hyperspectral Image Classification. Remote Sens 12(12):2035

    Article  Google Scholar 

  16. Roy SK et al (2020) HybridSN: Exploring 3-D–2-D CNN Feature Hierarchy for Hyperspectral Image Classification. IEEE Geosci Remote Sens Lett 17(2):277–281

    Article  MathSciNet  Google Scholar 

  17. Roy SK, Manna S, Song T, Bruzzone L (2020) Attention-based adaptive spectral–spatial kernel ResNet for hyperspectral image classification. IEEE Trans Geosci Remote Sens 59(9):7831–7843

    Article  Google Scholar 

  18. Yao W, Lian C, Bruzzone L (2021) ClusterCNN: Clustering-Based Feature Learning for Hyperspectral Image Classification. IEEE Geosci Remote Sens Lett 18(11):1991–1995

    Article  Google Scholar 

  19. Chen S, Jin M, Ding J (2021) Hyperspectral remote sensing image classification based on dense residual three-dimensional convolutional neural network. Multimed Tools Appl 80:1859–1882

    Article  Google Scholar 

  20. Zhang C, Li G, Du S (2019) Multi-Scale Dense Networks for Hyperspectral Remote Sensing Image Classification. IEEE Trans Geosci Remote Sens 57(11):9201–9222

    Article  Google Scholar 

  21. Zhang X (2023) Improved Three-Dimensional Inception Networks for Hyperspectral Remote Sensing Image Classification. IEEE Access 11:32648–32658

    Article  Google Scholar 

  22. Li M, Lu Y, Cao S, Wang X, Xie S (2023) A Hyperspectral Image Classification Method Based on the Nonlocal Attention Mechanism of a Multiscale Convolutional Neural Network. Sensors 23(6):3190

    Article  Google Scholar 

  23. Li C et al (2021) Hybrid Dilated Convolution with Multi-Scale Residual Fusion Network for Hyperspectral Image Classification. Micromachines 12(5):545

    Article  Google Scholar 

  24. Cui B, Dong XM, Zhan Q, Peng J, Sun W (2022) LiteDepthwiseNet: A Lightweight Network for Hyperspectral Image Classification. IEEE Trans Geosci Remote Sens 60:1–15

    Google Scholar 

  25. Shi C, Liao D, Zhang T, Wang L (2022) Hyperspectral Image Classification Based on Expansion Convolution Network. IEEE Trans Geosci Remote Sens 60:1–16

    Google Scholar 

  26. Tu C et al (2023) Hyperspectral image classification based on residual dense and dilated convolution. Infrared Phys Technol 131:104706

    Article  Google Scholar 

  27. Ahmad M, Khan AM, Mazzara M, Distefano S, Ali M, Sarfraz MS (2022) A Fast and Compact 3-D CNN for Hyperspectral Image Classification. IEEE Geosci Remote Sens Lett 19:1–5

    Article  Google Scholar 

  28. Shahshahani BM, Landgrebe DA (1994) The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon. IEEE Trans Geosci Remote Sens 32(5):1087–1095

    Article  Google Scholar 

  29. Liang D et al (2018) Understanding Mixup Training Methods. IEEE. Access 6:58774–58783

    Article  Google Scholar 

  30. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition.  2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 770–778. https://doi.org/10.1109/CVPR.2016.90

  31. Xu H, Yao W, Cheng L, Li B (2021) Multiple Spectral Resolution 3D Convolutional Neural Network for Hyperspectral Image Classification. Remote Sens 13(7):1248

    Article  Google Scholar 

  32. Wang T, Sun M,  Hu K (2017) Dilated deep residual network for image denoising. 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI), pp 1272–1279. https://doi.org/10.1109/ICTAI.2017.00192

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Authors and Affiliations

  1. The Higher Educational Key Laboratory for Measuring & Control Technology and Instrumentations of Heilongjiang Province, Harbin University of Science and Technology, Harbin, 150000, China

    Kun Sun, Yanli Yin, Fuxuan Dong & Xiaoming Sun

  2. Demonstration Center for Measurement and Control Technology and Instrumentation, Harbin University of Science and Technology, National Experimental Teaching, Harbin, 150000, China

    Kun Sun, Yanli Yin, Fuxuan Dong & Xiaoming Sun

Authors
  1. Kun Sun
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  2. Yanli Yin
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  3. Fuxuan Dong
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  4. Xiaoming Sun
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Correspondence to Kun Sun.

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Sun, K., Yin, Y., Dong, F. et al. Hyperspectral classification method based on M-ResHSDC. Multimed Tools Appl 83, 49767–49785 (2024). https://doi.org/10.1007/s11042-023-17515-y

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  • DOI: https://doi.org/10.1007/s11042-023-17515-y

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