Skip to main content
SpringerLink
Log in

On Endmember Detection in Hyperspectral Images with Morphological Associative Memories

  • Conference paper
  • First Online:
Advances in Artificial Intelligence — IBERAMIA 2002 (IBERAMIA 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2527))

Included in the following conference series:

Abstract

Morphological Associative Memories (MAM) are a construct similar to Hopfield Associative Memories defined on the (R,+, ∨, ⋀) algebraic system. The MAM posses excellent recall properties for undistorted patterns. However they suffer from the sensitivity to specific noise models, that can be characterized as erosive and dilative noise. We find that this sensitivity may be made of use in the task of Endmember determination for the Spectral Unmixingof Hyperspectral Images.

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 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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Asano A., K. Matsumura, K. Itoh, Y. Ichioka, S. Yokozeki, (1995) Optimization of morphological filters by learning, Optics Comm. 112: 265–270

    Article  Google Scholar 

  2. Carpenter G.A., S. Grossberg, D.B. Rosen, (1991) Fuzzy ART: Fast stable learning of analogpatterns by an adaptive resonance system, Neural Networks 4:759–771

    Article  Google Scholar 

  3. Crespo J.L., R. Duro, Hyperspectral Image Segmentation through Gaussian Synapse Networks, submitted ICANN’2002

    Google Scholar 

  4. Craig M., Minimum volume transformations for remotely sensed data, IEEE Trans. Geos. Rem. Sensing, 32(3):542–552

    Google Scholar 

  5. Gader P.D., M.A. Khabou, A. Kodobobsky (2000) Morphological regularization neural networks, Pattern Recognition 33 pp. 935–944

    Article  Google Scholar 

  6. Graña M., B. Raducanu. (2001) On the application of morphological heteroassociative neural networks. Proc. Int. Conf. on Image Processing (ICIP), I. Pitas (ed.), pp. 501–504, Thessaloniki, Greece, October, IEEE Press.

    Google Scholar 

  7. Hopfield J.J., (1982) Neural networks and physical systems with emergent collective computational abilities, Proc. Nat. Acad. Sciences, vol. 79, pp. 2554–2558

    MathSciNet  Google Scholar 

  8. Ifarraguerri A., C.-I Chang, (1999) Multispectral and Hyperspectral Image Analysis with Convex Cones, IEEE Trans. Geos. Rem. Sensing, 37(2):756–770

    Article  Google Scholar 

  9. Keshava N., J.F. Mustard Spectral unimixing, IEEE Signal Proc. Mag. 19(1) pp. 44–57 (2002)

    Google Scholar 

  10. Kohonen T., (1972) Correlation Matrix Memory, IEEE Trans. Computers, 21:353–359

    Article  MATH  Google Scholar 

  11. Landgrebe D., Indian Pines AVIRIS Hyperspectral Reflectance Data: 92av3c, 1992. The full data set is called Indian Pines 1 920612B available at http://makalu.jpl.nasa.gov/locator/index.html.

  12. Pessoa L.F.C, P. Maragos, (1998) MRL-filters: a general class of nonlinear systems and their optimal design for image processing, IEEE Trans. on Image Processing, 7(7): 966–978

    Article  Google Scholar 

  13. Pessoa L.F.C, P. Maragos (2000) Neural Networks with hybrid morphological/rank/linear nodes: a unifyingframew ork with applications to handwritten character recognition, Patt. Rec. 33: 945–960

    Article  Google Scholar 

  14. Raducanu B., M. Graña, P. Sussner.(2001) Morphological neural networks for vision based self-localization. Proc. of ICRA2001, Int. Conf. on Robotics and Automation, pp. 2059–2064, Seoul, Korea, May, IEEE Press.

    Google Scholar 

  15. Rand R.S., D.M. Keenan (2001) A Spectral Mixture Process Conditioned by Gibbs-Based Partitioning, IEEE Trans. Geos. Rem. Sensing, 39(7):1421–1434

    Article  Google Scholar 

  16. Ritter G. X., J. L. Diaz-de-Leon, P. Sussner. (1999) Morphological bidirectional associative memories. Neural Networks, Volume 12, pages 851–867

    Article  Google Scholar 

  17. Ritter G. X., P. Sussner, J. L. Diaz-de-Leon. (1998) Morphological associative memories. IEEE Trans. on Neural Networks, 9(2):281–292

    Article  Google Scholar 

  18. Ritter G.X., G. Urcid, L. Iancu, (2002) Reconstruction of patterns from moisy inputs using morphological associative memories, J. Math. Imag. Vision submitted

    Google Scholar 

  19. Ritter G. X., J.N. Wilson, Handbook of Computer Vision Algorithms in Image Algebra, Boca Raton, Fla: CRC Press

    Google Scholar 

  20. Rizzi A., M., F.M. Frattale Mascioli, (2002) Adaptive resolution Min-Max classifiers, IEEE trans Neural Networks 13 (2):402–414.

    Article  Google Scholar 

  21. Salembier P. (1992) Structuringelemen t adaptation for morphological filters, J. Visual Comm. Image Repres. 3: 115–136

    Article  Google Scholar 

  22. Sussner P., (2001) Observations on Morphological Associative Memories and the Kernel Method, Proc. IJCNN’2001, Washington DC, July

    Google Scholar 

  23. Sussner P. (2002), Generalizingop erations of binary autoassociative morphological memories usingfuzzy set theory, J. Math. Imag. Vision submitted

    Google Scholar 

  24. Tadjudin, S. and D. Landgrebe, (1998). Classification of High Dimensional Data with Limited TrainingSamples. PhD Thesis and School of Electrical & Computer Engineering Technical Report TR-ECE 98-8, Purdue University.

    Google Scholar 

  25. Tadjudin, S. and D. Landgrebe,(1999) Covariance Estimation with Limited TrainingSamples, IEEE Trans. Geos. Rem. Sensing, 37(4):2113–2118

    Article  Google Scholar 

  26. Tadjudin, S. and D. Landgrebe, (2000) Robust Parameter Estimation for Mixture Model, IEEE Trans. Geos. Rem. Sensing, 38(1):439–445

    Article  Google Scholar 

  27. Won Y., P. D. Gader, P.C. Cofield, (1997) Morphological shared-weight neural network with applications to automatic target detection, IEEE Trans. Neural Networks, 8(5): 1195–1203

    Article  Google Scholar 

  28. Yang P.F., P. Maragos, (1995) Min-Max Classifiers: learnability, design and application, Patt. Rec. 28(6):879–899

    Article  Google Scholar 

  29. Zhang X.; C. Hang; S. Tan; PZ. Wang, (1996) The min-max function differentiation and trainingof fuzzy neural networks, IEEE trans. Neural Networks 7(5):1139–1150

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. CCIA, Universidad Pais Vasco, Spain

    Manuel Graña

  2. Institute of Mathematics, Statistics and Scientific Computing, State University of Campinas, Brazil

    Bogdan Raducanu

  3. Center for Computer Vision and Visualization, University of Florida, USA

    Peter Sussner & G. Ritter

Authors
  1. Manuel Graña
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bogdan Raducanu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Peter Sussner
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. G. Ritter
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Telefónica Investigación y Desarrollo, Emilio Vargas 6, 28043, Madrid, Spain

    Francisco J. Garijo

  2. Dpto. Lenguajes y Sistemas Informáticos, Universidad de Sevilla, ETS Ingeniería Informática, 41012, Seville, Spain

    José C. Riquelme  & Miguel Toro  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Graña, M., Raducanu, B., Sussner, P., Ritter, G. (2002). On Endmember Detection in Hyperspectral Images with Morphological Associative Memories. In: Garijo, F.J., Riquelme, J.C., Toro, M. (eds) Advances in Artificial Intelligence — IBERAMIA 2002. IBERAMIA 2002. Lecture Notes in Computer Science(), vol 2527. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36131-6_54

Download citation

  • DOI: https://doi.org/10.1007/3-540-36131-6_54

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00131-7

  • Online ISBN: 978-3-540-36131-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation