Skip to main content

Small Target Detection Improvement in Hyperspectral Image

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8192))

Abstract

Target detection is an important issue in the HyperSpectral Image (HSI) processing field. However, current spectral-identification-based target detection algorithms are sensitive to the noise and most denoising algorithms cannot preserve small targets, therefore it is necessary to design a robust detection algorithm that can preserve small targets. This paper utilizes the recently proposed multidimensional wavelet packet transform with multiway Wiener filter (MWPT-MWF) to improve the target detection efficiency of HSI with small targets in the noise environment. The performances of the our method are exemplified using simulated and real-world HSI.

The original version of this chapter was revised: The copyright line was incorrect. This has been corrected. The Erratum to this chapter is available at DOI: 10.1007/978-3-319-02895-8_64

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Acito, N., Diani, M., Corsini, G.: A new algorithm for robust estimation of the signal subspace in hyperspectral images in the presence of rare signal components. IEEE Trans. Geosci. Remote Sens. 47(11), 3844–3856 (2009)

    Article  Google Scholar 

  2. Basedow, R.W., Carmer, D.C., Anderson, M.E.: Hydice system: Implementation and performance. In: SPIE’s 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, pp. 258–267. International Society for Optics and Photonics (1995)

    Google Scholar 

  3. Bourennane, S., Fossati, C., Cailly, A.: Improvement of classification for hyperspectral images based on tensor modeling. IEEE Geosci. Remote Sens. Lett. 7(4), 801–805 (2010)

    Article  Google Scholar 

  4. Bourennane, S., Fossati, C., Cailly, A.: Improvement of target detection based on tensorial modelling (2010)

    Google Scholar 

  5. Cichocki, A., Zdunek, R., Phan, A., Amari, S.: Nonnegative matrix and tensor factorizations: applications to exploratory multi-way data analysis and blind source separation. Wiley, New Jersey (2009)

    Book  Google Scholar 

  6. Daubechies, I.: Ten lectures on wavelets. SIAM (2006)

    Google Scholar 

  7. De Lathauwer, L., De Moor, B., Vandewalle, J.: A multilinear singular value decomposition. SIAM J. Matrix Anal. Appl. 21(4), 1253–1278 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  8. De Lathauwer, L., De Moor, B., Vandewalle, J.: On the best rank-1 and rank-(r1, r2,..., rn) approximation of higher-order tensors. SIAM J. Matrix Anal. Appl. 21(4), 1324–1342 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  9. Jin, X., Paswaters, S., Cline, H.: A comparative study of target detection algorithms for hyperspectral imagery. In: SPIE Defense, Security, and Sensing, p. 73341W–73341W. International Society for Optics and Photonics (2009)

    Google Scholar 

  10. Kerekes, J., Baum, J.: Hyperspectral imaging system modeling. Linc. Lab. J. 14(1), 117–130 (2003)

    Google Scholar 

  11. Kuybeda, O., Malah, D., Barzohar, M.: Rank estimation and redundancy reduction of high-dimensional noisy signals with preservation of rare vectors. IEEE Trans. Signal Process. 55(12), 5579–5592 (2007)

    Article  MathSciNet  Google Scholar 

  12. Letexier, D., Bourennane, S.: Noise removal from hyperspectral images by multidimensional filtering. IEEE Trans. Geosci. Remote Sens. 46(7), 2061–2069 (2008)

    Article  Google Scholar 

  13. Letexier, D., Bourennane, S., Blanc-Talon, J.: Nonorthogonal tensor matricization for hyperspectral image filtering. IEEE Geosci. Remote Sens. Lett. 5(1), 3–7 (2008)

    Article  Google Scholar 

  14. Lin, T., Bourennane, S.: Hyperspectral image processing by jointly filtering wavelet component tensor. IEEE Trans. Geosci. Remote Sens. 51(6), 3529–3541 (2013)

    Article  Google Scholar 

  15. Manolakis, D., Marden, D., Shaw, G.A.: Hyperspectral image processing for automatic target detection applications. Linc. Lab. J. 14(1), 79–116 (2003)

    Google Scholar 

  16. Muti, D., Bourennane, S.: Multidimensional filtering based on a tensor approach. Signal Process. 85(12), 2338–2353 (2005)

    Article  MATH  Google Scholar 

  17. Renard, N., Bourennane, S.: Improvement of target detection methods by multiway filtering. IEEE Trans. Geosci. Remote Sens. 46(8), 2407–2417 (2008)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lin, T., Marot, J., Bourennane, S. (2013). Small Target Detection Improvement in Hyperspectral Image. In: Blanc-Talon, J., Kasinski, A., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2013. Lecture Notes in Computer Science, vol 8192. Springer, Cham. https://doi.org/10.1007/978-3-319-02895-8_41

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-02895-8_41

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02894-1

  • Online ISBN: 978-3-319-02895-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics