Skip to main content
SpringerLink
Log in

Probabilistic PCA Based Hyper Spectral Image Classification for Remote Sensing Applications

  • Conference paper
  • First Online:
Intelligent Systems Design and Applications (ISDA 2018 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 941))

Abstract

Hyper spectral image (HSI) Classification has become important research areas of remote sensing which can be used in many practical applications, including precision agriculture, Land cover mapping, environmental monitoring etc. HSI Classification includes various steps like Noise removal, dimensionality reduction, and classification. In this work, we adopted structure-preserving recursive filter (SPRF) to noise removal and Probabilistic based principal component analysis (PPCA) is applied to reduce dimensionality. Finally classification is performed using multi class large marginal distribution machine (LDM). The proposed (HSI) Classification method is carried out and results are validated across the three widely used standard datasets like Indian Pines, University of Pavia and Salinas. The obtained results show that the proposed method provides results on par with similar type of methods from literature.

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

Access this chapter

Log in via an 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

Institutional subscriptions

References

  1. Zhan, K., Wang, H., Huang, H., Xie, Y.: Large margin distribution machine for hyper spectral image classification. J. Electron. Imaging 25(6), 063024 (2016). https://doi.org/10.1117/1.JEI.25.6.063024

    Article  Google Scholar 

  2. Gastal, E.S., Oliveira, M.M.: Domain transform for edge-aware image and video processing. ACM Trans. Graph. 30(4), 69 (2011)

    Article  Google Scholar 

  3. Zhang, T., Zhou, Z.-H.: Large margin distribution machine. In: Proceedings of SIGKDD, International Conference on Knowledge Discovery and Data Mining, vol. 20, pp. 313–322 (2014)

    Google Scholar 

  4. van der Maaten, L.J.P., Postma, E.O., van den Herik, H.J.: Dimensionality reduction: a comparative review. Tilburg University Technical report, TiCC-TR 2009-005, 2009

    Google Scholar 

  5. Tipping, M.E., Bishop, C.M.: Probabilistic principal component analysis. J. R. Stat. Soc.: Ser. B 61(3), 611–622 (1999)

    Article  MathSciNet  Google Scholar 

  6. Rodarmel, C., Shan, J.: Principal component analysis for hyper spectral image classification. Purdue University, West Lafayette, 47907-1284, U.S.A (2002)

    Google Scholar 

  7. Chen, Y., Lin, Z., Zhao, X.: Deep learning-based classification of hyperspectral data. IEEE J. 7(6), 2094–2107 (2014)

    Google Scholar 

  8. Kang, X., Li, S.: Feature extraction of hyperspectral images with image fusion and recursive filtering. IEEE Trans. Geosci. Remote Sens. 52, 3742–3752 (2014)

    Article  Google Scholar 

  9. Kumar, B., Dikshit, O.: Texture based hyperspectral image classification. In: The International Archives of the Photogrammetry (2014)

    Google Scholar 

  10. Diwaker, M.K., Chaudhary, P.T., Bhatt, A., Saxena, A.: A comparative performance analysis of feature extraction techniques for hyperspectral image classification. Int. J. Softw. Eng. Appl. 10(12), 179–188 (2016)

    Google Scholar 

  11. Zhan, K., Wang, H., Xie, Y., Zhang, C., Min, Y.: Albedo recovery for hyperspectral image classification. J. Electron. Imaging 26, 043010 (2017)

    Article  Google Scholar 

  12. Liu, Y., Wang, R., Zeng, Y.: An improvement of one-against-one method for multi-class support vector machine. In: 2007 International Conference on Machine Learning and Cybernetics, Hong Kong, pp. 2915–2920 (2007)

    Google Scholar 

  13. Zhang, N., Wang, M., Wang, N.: Precision agriculture-a worldwide overview. Comput. Electron. Agric. 36, 113–132 (2002)

    Article  Google Scholar 

  14. Yang, C., Everitt, J.H., Bradford, J.M.: Airborne hyperspectral imagery and yield monitor data for estimating grain sorghum yield variability. Trans. ASAE 47(3), 915–924 (2004)

    Article  Google Scholar 

  15. Yang, C.: Airborne hyperspectral imagery for mapping crop yield variability. Geogr. Compass 3(5), 1717–1731 (2009)

    Article  Google Scholar 

  16. Boggavarapu, L.N.P., Prabukumar, M.: Survey on classification méthodes for hyper spectral remote sensing imagery. In: International Conference on Intelligent Computing and Control Systems (2017)

    Google Scholar 

  17. Boggavarapu, L.N.P., Prabukumar, M.: Robust classification of hyperspectral remote sensing images combined with multihypothesis prediction and 3 dimensional discrete wavelet transform. Int. J. Pure Appl. Math. 117(17), 115–120 (2017)

    Google Scholar 

  18. Vaddi, R., Prabukumar, M.: Comparative study of feature extraction techniques for hyper spectral remote sensing image classification: a survey. In: 2017 International Conference on Intelligent Computing and Control Systems (ICICCS) (2017)

    Google Scholar 

  19. Kumar, C.A.: Analysis of unsupervised dimensionality reduction techniques. Comput. Sci. Inf. Syst. 6(2), 217–227 (2009)

    Article  Google Scholar 

  20. Prabukumar, M., Sawant, S., Samiappan, S., Agilandeeswari, L.: Three-dimensional discrete cosine transform-based feature extraction for hyperspectral image classification. J. Appl. Remote Sens. 12(4), 046010 (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information Technology Engineering (SITE), VIT University, Vellore, 632-014, India

    Radhesyam Vaddi & Prabukumar Manoharan

  2. Department of Information Technology, V. R. Siddhartha Engineering College, Vijayawada, India

    Radhesyam Vaddi

Authors
  1. Radhesyam Vaddi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Prabukumar Manoharan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Prabukumar Manoharan .

Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs, Auburn, WA, USA

    Ajith Abraham

  2. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Aswani Kumar Cherukuri

  3. Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

  4. Machine Intelligence Research Labs, Auburn, WA, USA

    Niketa Gandhi

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Vaddi, R., Manoharan, P. (2020). Probabilistic PCA Based Hyper Spectral Image Classification for Remote Sensing Applications. In: Abraham, A., Cherukuri, A., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 941. Springer, Cham. https://doi.org/10.1007/978-3-030-16660-1_84

Download citation

Publish with us

Policies and ethics

Search

Navigation