[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content
Springer Nature Link
Log in

Crop Hyperspectral Dataset Unmixing Using Modified U-Net Model

  • Conference paper
  • First Online:
Digital Business and Intelligent Systems (DB&IS 2024)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2157))

  • 166 Accesses

Abstract

The hyperspectral unmixing procedure allows the extraction of different material information (endmembers) and their abundances from pixel spectra of the hyperspectral image for further analysis. An extensive study of papers on hyperspectral unmixing methods that use variational autoencoders, deep convolutional networks, or visual transformers shows a lack of diversity in the hyperspectral imaging (HSI) datasets. In this paper, we propose using a modified U-Net architecture of a deep convolutional neural network to analyze a new UAV-gathered HSI dataset for crop health analysis. Secondly, a data analysis methodology was created to extract a set of endmembers with a variation in the samples from the original HSI data for ground truth creation. Our U-Net model was modified to extract endmember class data at the bottleneck layer, and the SoftMax activation function was used during the decoding phase to extract the abundance map. Multiplication of abundance and endmember matrices reconstructs the HSI upon which the reconstruction losses are calculated. We used an image-patching approach to improve the model’s ability to learn endmember data from the HSI dataset. The model performance was evaluated on our newly created data and openly available hyperspectral datasets such as Washington DC Mall, Samson, and Urban.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 89.99
Price includes VAT (United Kingdom)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 59.99
Price includes VAT (United Kingdom)
  • 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

Institutional subscriptions

Similar content being viewed by others

References

  1. Ahmad, M., et al.: Hyperspectral image classification-traditional to deep models: a survey for future prospects. IEEE J. Select. Topics Appl. Earth Observ. Remote Sens. 15, 968–999 (2022). https://doi.org/10.1109/jstars.2021.3133021

  2. Dale, L. M., et al.: Hyperspectral imaging applications in agriculture and agro-food product quality and safety control: a review. Appl. Spectrosc. Rev. (2013).https://doi.org/10.1080/05704928.2012.705800

  3. Bioucas-Dias, J.M., Plaza, A., Camps-Valls, G., Scheunders, P., Nasrabadi, N., Chanussot, J.: Hyperspectral remote sensing data analysis and future challenges. IEEE Geosci. Remote Sens. Magaz. 1(2), 6–36 (2013). https://doi.org/10.1109/MGRS.2013.2244672

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

  5. Adão, T., et al.: Hyperspectral imaging: a review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote Sens. 9(11), 1110 (2017). https://doi.org/10.3390/rs9111110

  6. Mahesh, S., Jayas, D.S., Paliwal, J., White, N.D.G.: Hyperspectral imaging to classify and monitor quality of agricultural materials. J. Stored Prod. Res. 61, 17–26 (2015). https://doi.org/10.1016/j.jspr.2015.01.006

  7. Wang, C., Liu, B., Liu, L., et al.: 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

  8. Paura, V., Marcinkevicius, V.: Benchmark for hyperspectral unmixing algorithm evaluation. Informatica 34(2), 285–315 (2023). https://doi.org/10.15388/23-INFOR522

  9. Bioucas-Dias, J.M., Figueiredo, M.A.T.: Alternating direction algorithms for constrained sparse regression: application to hyperspectral unmixing. In: 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (2010). https://doi.org/10.1109/WHISPERS.2010.5594963

  10. Iordache, M., Bioucas-Dias, J.M., Plaza, A.: Total variation spatial regularization for sparse Hyperspectral unmixing. IEEE Trans. Geosci. Remote Sens. 50(11), 4484–4502 (2012). https://doi.org/10.1109/TGRS.2012.2191590

  11. Zhang, S., Li, J., Li, H.-C., Deng, C., Plaza, A.: Spectral–spatial weighted sparse regression for hyperspectral image unmixing. IEEE Trans. Geosci. Remote Sens. 56(6), 3265–3276 (2018). https://doi.org/10.1109/TGRS.2018.2797200

  12. Su, H., Jia, C., Zheng, P., Du, Q.: Superpixel-based weighted collaborative sparse regression and reweighted low-rank representation for hyperspectral image unmixing. IEEE J. Select. Topics Appl. Earth Observ. Remote Sens. 15, 393–408 (2022). https://doi.org/10.1109/JSTARS.2021.3133428

  13. Wang, X., Zhong, Y., Zhang, L., Xu, Y.: Spatial group sparsity regularized nonnegative matrix factorization for hyperspectral unmixing. IEEE Trans. Geosci. Remote Sens. 55(11), 6287–6304 (2017). https://doi.org/10.1109/TGRS.2017.2724944

  14. Wang, J.-J., Wang, D.-C., Huang, T.-Z., Huang, J.: Endmember constraint non-negative tensor factorization via total variation for hyperspectral unmixing. In: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARS (2021). https://doi.org/10.1109/IGARSS47720.2021.9554468

  15. Feng, X.-R., Li, H.-C., Liu, S., Zhang, H.: Correntropy-based autoencoder-like NMF with total variation for hyperspectral unmixing. IEEE Geosci. Remote Sens. Lett. 19, 1–5 (2022). https://doi.org/10.1109/LGRS.2020.3020896

  16. Borsoi, R.A., Imbiriba, T., Bermudez, J.C.M.: Deep generative endmember modeling: an application to unsupervised spectral unmixing. IEEE Trans. Computat. Imaging 6, 374–384 (2020). https://doi.org/10.1109/TCI.2019.2948726

  17. Han, Z., Hong, D., Gao, L., Zhang, B., Chanussot, J.: deep half-siamese networks for hyperspectral unmixing. IEEE Geosci. Remote Sens. Lett. 18(11), 1996–2000 (2021). https://doi.org/10.1109/LGRS.2020.3011941

  18. Zhao, M., Yan, L., Chen, J.: LSTM-DNN based autoencoder network for nonlinear hyperspectral image unmixing. IEEE J. Select. Topics Signal Process. 15(2), 295–309 (2021). https://doi.org/10.1109/JSTSP.2021.3052361

  19. Chen, J., Li, J., Gamba, P.: A multi-tasks autoencoder hyperspectral unmixing model with information gain based on graph network. In: 2023 13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), Athens, pp. 1–5 (2023). https://doi.org/10.1109/WHISPERS61460.2023.10430667

  20. Shu, S., Huang, T.-Z., Huang, J.: Dual-channel enhanced decoder network for blind hyperspectral unmixing. IEEE Geosci. Remote Sens. Lett. 21, 5500405(1–5) (2024). https://doi.org/10.1109/LGRS.2023.3338193

  21. Ghosh, P., Roy, S.K., Koirala, B., Rasti, B., Scheunders, P.: Hyperspectral unmixing using transformer network. IEEE Trans. Geosci. Remote Sens. 60, 553511(1–16) (2022). https://doi.org/10.1109/TGRS.2022.3196057

  22. Kokaly, R.F., et al.: USGS Spectral Library Version 7. In: Survey, U.S. Data Series (2017). https://doi.org/10.3133/ds1035

  23. Hyperspectral Datasets. http://lesun.weebly.com/hyperspectral-data-set.html. Accessed 15 Mar 2024

  24. Hyperspectral Images - MultiSpec. https://engineering.purdue.edu/~biehl/MultiSpec/hyperspectral.html. Accessed 15 Mar 2024

  25. Specim AFX10 Hyperspectral Camera. https://www.specim.com/products/specim-afx10/. Accessed 15 Mar 2024

  26. CIE 1931 Color Space. https://en.wikipedia.org/wiki/CIE_1931_color_space/. Accessed 10 Mar 2024

  27. Nascimento, J.M.P., Dias, J.M.B.: Vertex component analysis: a fast algorithm to unmix hyperspectral data. IEEE Trans. Geosci. emote Sens. 43(4), 898–910 (2005). https://doi.org/10.1109/TGRS.2005.844293

  28. OneCycleLR. https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.OneCycleLR.html. Accessed 12 Mar 2024

  29. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. arXiv preprint arXiv:1505.04597 (2015)

Download references

Author information

Authors and Affiliations

  1. Institute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania

    Vytautas Paura & Virginijus Marcinkevičius

Authors
  1. Vytautas Paura
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Virginijus Marcinkevičius
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vytautas Paura .

Editor information

Editors and Affiliations

  1. Institute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania

    Audronė Lupeikienė

  2. CUI, University of Geneva, Carouge, Switzerland

    Jolita Ralyté

  3. Institute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania

    Gintautas Dzemyda

Rights and permissions

Reprints and permissions

Copyright information

© 2024 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

Paura, V., Marcinkevičius, V. (2024). Crop Hyperspectral Dataset Unmixing Using Modified U-Net Model. In: Lupeikienė, A., Ralyté, J., Dzemyda, G. (eds) Digital Business and Intelligent Systems. DB&IS 2024. Communications in Computer and Information Science, vol 2157. Springer, Cham. https://doi.org/10.1007/978-3-031-63543-4_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-63543-4_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-63542-7

  • Online ISBN: 978-3-031-63543-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation