Skip to main content
SpringerLink
Log in
Book cover

International Conference on Advances in Computing Research

ACR 2023: Proceedings of the 2023 International Conference on Advances in Computing Research (ACR’23) pp 155–168Cite as

Hybrid Capsule Network for Hyperspectral Image Unmixing and Classification

  • 257 Accesses

Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 700)

Abstract

Hyperspectral unmixing identifies and quantifies the materials in each mixed pixel of a hyperspectral image arising from low spatial resolution. However, most of the methods for unmixing use spectral information and disregard rich spatial information. At the same time, a few methods that use spatial information find difficulty representing the spatial-spectral features because of the high dimensionality of the hyperspectral image. So, deeper architectures are used, but such deep architectures find converging difficult. This article proposes a new method for hyperspectral unmixing that utilizes a capsule network and a 3D convolutional neural network. Using a capsule network allows for encoding rich features such as spatial information, spectral signature, and possible affine transformations of spectra using vectors rather than scalars. Hence, assuming the linear mixing model and enforcing appropriate constrain on the considered method, blind hyperspectral unmixing has been done. The performance of a proposed method was evaluated using Spectral Angle Distance (SAD) and Mean Square Error (MSE) on three datasets: Jasper Ridge, Samson, and Urban. The mean SAD and MSE values for each dataset were as follows: Jasper Ridge - 0.04259 and 0.01289; Samson - 0.02599 and 0.00370; Urban - 0.04954 and 0.02084. The results show that the proposed hybrid capsule network, which uses the spatial-spectral feature, performed well, with low SAD and MSE values, indicating that it has well-estimated reflectance and fractional abundance.

Keywords

  • Hybrid capsule network
  • hyperspectral image unmixing
  • spectral-spatial feature
  • 3D CNN
  • abundance map

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Goetz, A.F.H., et al.: Imaging spectrometry for earth remote sensing. Science 228(4704), 1147–1153 (1985). https://doi.org/10.1126/science.228.4704.1147

  2. Bioucas-Dias, J.M., et al.: Hyperspectral unmixing overview: geometrical, statistical, and sparse regression-based approaches. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 5(2), 354–379 (2012). https://doi.org/10.1109/JSTARS.2012.2194696

  3. Keshava, N., Mustard, J.F.: Spectral unmixing. IEEE Signal Process. Mag. 19(1), 44–57 (2002). https://doi.org/10.1109/79.974727

  4. Paoletti, M.E., et al.: Capsule networks for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 57(4), 2145–2160 (2019). https://doi.org/10.1109/TGRS.2018.2871782

  5. Hughes, G.: On the mean accuracy of statistical pattern recognizers. IEEE Trans. Inf. Theory 14(1), 55–63 (1968). https://doi.org/10.1109/TIT.1968.1054102

  6. Chen, Y., et al.: Deep learning-based classification of hyperspectral data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7(6), 2094–2107 (2014). https://doi.org/10.1109/JSTARS.2014.2329330

  7. Li, T., Zhang, J., Zhang, Y.: Classification of hyperspectral image based on deep belief networks. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 5132–5136 (2014). https://doi.org/10.1109/ICIP.2014.7026039

  8. Ma, X., Wang, H., Geng, J.: Spectral-spatial classification of hyperspectral image based on deep auto-encoder. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 9(9), 4073–4085 (2016). https://doi.org/10.1109/JSTARS.2016.2517204

  9. Chen, Y., et al.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans. Geosci. Remote Sens. 54(10), 6232–6251 (2016). https://doi.org/10.1109/TGRS.2016.2584107

  10. Wang, J., et al.: Dual-channel capsule generation adversarial network for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 60, 1–16 (2022). https://doi.org/10.1109/TGRS.2020.3044312

  11. Guo, R., Wang, W., Qi, H.: Hyperspectral image unmixing using autoencoder cascade. In: 2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), pp. 1–4 (2015). https://doi.org/10.1109/WHISPERS.2015.8075378

  12. Palsson, B., Sveinsson, J.R., Ulfarsson, M.O.: Spectral-spatial hyperspectral unmixing using multitask learning. IEEE Access 7, 148861–148872 (2019). https://doi.org/10.1109/ACCESS.2019.2944072

    CrossRef  Google Scholar 

  13. Palsson, B., Ulfarsson, M.O., Sveinsson, J.R.: Convolutional autoencoder for spectral-spatial hyperspectral unmixing. IEEE Trans. Geosci. Remote Sens. 59(1), 535–549 (2021). https://doi.org/10.1109/TGRS.2020.2992743

  14. Ji, S., et al.: 3D convolutional neural networks for human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 221–231 (2013). https://doi.org/10.1109/TPAMI.2012.59

  15. He, K., et al.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification (2015). 1502.01852. https://arxiv.org/abs/1502.01852

  16. Hinton, G.E., Krizhevsky, A., Wang, S.D.: Transforming auto-encoders. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds.) ICANN 2011. LNCS, vol. 6791, pp. 44–51. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21735-7_6

    CrossRef  Google Scholar 

  17. Sabour, S., Frosst, N., Hinton, G.E.: Dynamic Routing Between Capsules (2017). https://doi.org/10.48550/ARXIV.1710.09829. https://arxiv.org/abs/1710.09829

  18. Palsson, B., et al.: Hyperspectral unmixing using a neural network autoencoder. IEEE Access 6, 25646–25656 (2018). https://doi.org/10.1109/ACCESS.2018.2818280

Download references

Author information

Authors and Affiliations

  1. Institute of Engineering, Lalitpur, Nepal

    Ravi Giri & Dibakar Raj Pant

  2. University of Turku, Turku, Finland

    Jukka Heikkonen

  3. Savonia University of Applied Sciences, Kuopio, Finland

    Rajeev Kanth

Authors
  1. Ravi Giri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dibakar Raj Pant
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jukka Heikkonen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rajeev Kanth
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dibakar Raj Pant .

Editor information

Editors and Affiliations

  1. University of Detroit Mercy, Detroit, MI, USA

    Kevin Daimi

  2. Charles Sturt University, Sydney, NSW, Australia

    Abeer Al Sadoon

Rights and permissions

Reprints and Permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Giri, R., Pant, D.R., Heikkonen, J., Kanth, R. (2023). Hybrid Capsule Network for Hyperspectral Image Unmixing and Classification. In: Daimi, K., Al Sadoon, A. (eds) Proceedings of the 2023 International Conference on Advances in Computing Research (ACR’23). ACR 2023. Lecture Notes in Networks and Systems, vol 700. Springer, Cham. https://doi.org/10.1007/978-3-031-33743-7_13

Download citation

search

Navigation