Skip to main content
Book cover

Advanced Computing and Intelligent Technologies pp 89–97Cite as

Classification of Land Cover Hyperspectral Images Using Deep Convolutional Neural Network

  • 338 Accesses

Part of the Lecture Notes in Electrical Engineering book series (LNEE,volume 914)

Abstract

In this research, we proposed a novel deep convolutional neural network (DCNN) for land cover hyperspectral image classification. The Indian pines dataset was used to train and test the performance of the proposed model. Data augmentation, up-sampling, and zero-padding techniques were used to enhance the quality and quantity of the dataset. The proposed model was trained on the enhanced dataset using a graphical processing unit (GPU) environment. The trained model was tested using a test dataset and produced an accuracy of 99.3%. The testing accuracy of the proposed DCNN model was superior to other state-of-the-art machine learning techniques such as logistic regression (LR), decision tree (DT), support vector machine (SVM), and multilayer perceptron (MLP).

Keywords

  • Land cover images
  • Hyperspectral images
  • Deep convolutional neural network
  • Data augmentation
  • Data up-sampling

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-981-19-2980-9_8
  • Chapter length: 9 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   269.00
Price excludes VAT (USA)
  • ISBN: 978-981-19-2980-9
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book
USD   349.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

References

  1. Wang, C., Liu, B., Liu, L., Zhu, Y., Hou, J., Liu, P., Li, X.: A review of deep learning used in the hyperspectral image analysis for agriculture. Artif. Intell. Rev. 54, 5205–5253 (2021). https://doi.org/10.1007/s10462-021-10018-y

    CrossRef  Google Scholar 

  2. Kavitha, K., Arivazhagan, S., Kanaga Sangeetha, I.: Hyperspectral image classification using support vector machine in Ridgelet domain. Natl. Acad. Sci. Lett. 38, 475–478 (2015). https://doi.org/10.1007/s40009-015-0361-9

    CrossRef  Google Scholar 

  3. Liu, L., Li, C., Lei, Y., Yin, J., Zhao, J.: Feature extraction for hyperspectral remote sensing image using weighted PCA-ICA. Arab. J. Geosci. 10, 307 (2017). https://doi.org/10.1007/s12517-017-3090-1

    CrossRef  Google Scholar 

  4. Baumgardner, M.F., Biehl, L.L., Landgrebe, D.A.: 220 Band AVIRIS hyperspectral image data set, 12 June 1992 Indian Pine Test Site 3(2015). https://purr.purdue.edu/publications/1947/1. https://doi.org/10.4231/R7RX991C

  5. Mei, S., Geng, Y., Hou, J., Du, Q.: Learning hyperspectral images from RGB images via a coarse-to-fine CNN. Sci. China Inf. Sci. 65 (2022). https://doi.org/10.1007/s11432-020-3102-9

  6. Fu, H., Sun, G., Ren, J., Zhang, A., Jia, X.: Fusion of PCA and segmented-PCA domain multiscale 2-D-SSA for effective spectral-spatial feature extraction and data classification in hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 60 (2022). https://doi.org/10.1109/TGRS.2020.3034656

  7. Pande, S., Banerjee, B.: HyperLoopNet: Hyperspectral image classification using multiscale self-looping convolutional networks. ISPRS J. Photogramm. Remote Sens. 183, 422–438 (2022). https://doi.org/10.1016/j.isprsjprs.2021.11.021

    CrossRef  Google Scholar 

  8. Gao, T., Chandran, A.K.N., Paul, P., Walia, H., Yu, H.: Hyperseed: An end-to-end method to process hyperspectral images of seeds. Sensors 21 (2021). https://doi.org/10.3390/s21248184

  9. Mehalli, Z., Zigh, E., Loukil, A., Ali Pacha, A.: Hyperspectral data preprocessing of the Northwestern Algeria Region BT—Networking. Intell. Syst. Secur. (2022).

    Google Scholar 

  10. Mukhopadhyay, M. et al.: Facial emotion recognition based on textural pattern and convolutional neural network. In: 2021 IEEE 4th International Conference on Computing, Power and Communication Technologies (GUCON), pp. 1–6. https://doi.org/10.1109/GUCON50781.2021.9573860

  11. Wang, C., Zhang, L., Wei, W., Zhang, Y.: Hyperspectral image classification with data augmentation and classifier fusion. IEEE Geosci. Remote Sens. Lett. 17, 1420–1424 (2020). https://doi.org/10.1109/LGRS.2019.2945848

    CrossRef  Google Scholar 

  12. Zeng, J., Hu, W., Huang, F.: Analysis of hyperspectral image classification technology and application based on convolutional neural networks. In: 2021 IEEE International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI), pp. 409–414 (2021). https://doi.org/10.1109/CEI52496.2021.9574493

  13. Bodapati, S., et al.: Comparison and analysis of RNN-LSTMs and CNNs for social reviews classification. In: Bansal, J.C., Fung, L.C.C., Simic, M., Ghosh, A. (eds.) Advances in Applications of Data-Driven Computing. Advances in Intelligent Systems and Computing, vol. 1319. Springer, Singapore (2021). https://doi.org/10.1007/978-981-33-6919-1_4

  14. Kumar, V., Singh, R.S., Dua, Y.: Morphologically dilated convolutional neural network for hyperspectral image classification. Signal Process. Image Commun. 101, 116549 (2022). https://doi.org/10.1016/j.image.2021.116549

Download references

Acknowledgements

The authors are grateful to Vel Tech Technology Business Incubator, Chennai.

Author information

Authors and Affiliations

  1. Department of Computer Science & Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, 600062, India

    J. Arun Pandian, Saurav Kr. Gupta, Rohit Kumar, Shourjya Hazra & K. Kanchanadevi

Authors
  1. J. Arun Pandian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Saurav Kr. Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rohit Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shourjya Hazra
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. K. Kanchanadevi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Saurav Kr. Gupta .

Editor information

Editors and Affiliations

  1. Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, India

    Rabindra Nath Shaw

  2. Regional Campus Manipur, Indira Gandhi National Tribal University, Imphal, Manipur, India

    Sanjoy Das

  3. Department of Computer Science, University of Milan, Milan, Italy

    Vincenzo Piuri

  4. Department of Information Engineering and Mathematics, University of Siena, Siena, Italy

    Monica Bianchini

Rights and permissions

Reprints and Permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Pandian, J.A., Gupta, S.K., Kumar, R., Hazra, S., Kanchanadevi, K. (2022). Classification of Land Cover Hyperspectral Images Using Deep Convolutional Neural Network. In: Shaw, R.N., Das, S., Piuri, V., Bianchini, M. (eds) Advanced Computing and Intelligent Technologies. Lecture Notes in Electrical Engineering, vol 914. Springer, Singapore. https://doi.org/10.1007/978-981-19-2980-9_8

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-2980-9_8

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-2979-3

  • Online ISBN: 978-981-19-2980-9

  • eBook Packages: Computer ScienceComputer Science (R0)