On Endmember detection in Hyperspectral Images with Morphological Associative Memories

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 2527, Issue , 2002, Pages 526-535

  Graña, Manuel ^a   Raducanu, Bogdan ^a   Sussner, Peter ^b   Ritter, Gerhard ^c  

^a UNIVERSITY OF THE BASQUE COUNTRY UPV EHU   (Spain)

^b UNIVERSITY OF CAMPINAS   (Brazil)

^c UNIVERSITY OF FLORIDA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGEBRAIC SYSTEM; ASSOCIATIVE MEMORIES; ENDMEMBERS; HYPER-SPECTRAL IMAGES; MORPHOLOGICAL ASSOCIATIVE MEMORIES; NOISE MODELS; ASSOCIATIVE MEMORY; MORPHOLOGICAL ASSOCIATIVE MEMORY; SPECTRAL UNMIXING;

ARTIFICIAL INTELLIGENCE; ASSOCIATIVE STORAGE; ASSOCIATIVE PROCESSING; SPECTROSCOPY;

ASSOCIATIVE PROCESSING; ASSOCIATIVE STORAGE;

EID: 79952267664     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/3-540-36131-6_54     Document Type: Conference Paper

References (29)

1. Asano A., K. Matsumura, K. Itoh, Y. Ichioka, S. Yokozeki, (1995) Optimization of morphological filters by learning, Optics Comm. 112 : 265-270
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
3. Crespo J.L., R. Duro, Hyperspectral Image Segmentation through Gaussian Synapse Networks , submitted ICANN'2002
4. Craig M., Minimum volume transformations for remotely sensed data, IEEE Trans. Geos. Rem. Sensing, 32(3):542-552
5. Gader P.D., M.A. Khabou, A. Kodobobsky (2000) Morphological regularization neural networks, Pattern Recognition 33 pp. 935-944
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.
7. Hopfield J.J., (1982) Neural networks and physical systems with emergent collective computational abilities, Proc. Nat. Acad. Sciences, vol. 79, pp. 2554-2558
8. Ifarraguerri A., C.-I Chang, (1999) Multispectral and Hyperspectral Image Analysis with Convex Cones, IEEE Trans. Geos. Rem. Sensing, 37(2):756-770
9. Keshava N., J.F. Mustard Spectral unimixing, IEEE Signal Proc. Mag. 19(1) pp. 44-57 (2002)
10. Kohonen T. , (1972) Correlation Matrix Memory, IEEE Trans. Computers, 21:353-359
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
13. Pessoa L.F.C , P. Maragos (2000) Neural Networks with hybrid morphological/ rank/linear nodes: a unifying frame work with applications to handwritten character recognition, Patt. Rec. 33: 945-960
14. Raducanu B. , M. Graña, P. Sussner.(2001) Morphological neural networks for vision based self-localization. Proc. of ICRA 2001, Int. Conf. on Robotics and Automation, pp. 2059-2064, Seoul, Korea, May , IEEE Press.
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
16. Ritter G. X., J. L. Diaz-de-Leon, P. Sussner. (1999) Morphological bidirectional associative memories. Neural Networks, Volume 12, pages 851-867
17. Ritter G. X., P. Sussner, J. L. Diaz-de-Leon. (1998) Morphological associative memories. IEEE Trans. on Neural Networks, 9(2):281-292
18. Ritter G.X., G. Urcid, L. Iancu, (2002) Reconstruction of patterns from moisy inputs using morphological associative memories, J. Math. Imag. Vision submitted
19. Ritter G. X., J.N. Wilson, Handbook of Computer Vision Algorithms in Image Algebra, Boca Raton, Fla: CRC Press
20. Rizzi A., M. ,F.M. Frattale Mascioli, (2002) Adaptive resolution Min-Max classifiers, IEEE trans Neural Networks 13 (2):402-414.
21. Salembier P. (1992) Structuringelemen t adaptation for morphological filters, J. Visual Comm. Image Repres. 3: 115-136
22. Sussner P., (2001) Observations on Morphological Associative Memories and the Kernel Method, Proc. IJCNN'2001, Washington DC, July
23. Sussner P. (2002) , Generalizingop erations of binary autoassociative morphological memories usingfuzzy set theory, J. Math. Imag. Vision submitted
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.
25. Tadjudin, S. and D. Landgrebe,(1999) Covariance Estimation with Limited TrainingSamples, IEEE Trans. Geos. Rem. Sensing, 37(4):2113-2118
26. Tadjudin, S. and D. Landgrebe, (2000) Robust Parameter Estimation for Mixture Model, IEEE Trans. Geos. Rem. Sensing, 38(1):439-445
27. Won Y., P. D. Gader, P.C. Coffield, (1997) Morphological shared-weight neural network with applications to automatic target detection, IEEE Trans. Neural Networks, 8(5): 1195-1203
28. Yang P.F., P. Maragos, (1995) Min-Max Classifiers: learnability, design and application, Patt. Rec. 28(6):879-899
29. Zhang X.; C. Hang; S. Tan; PZ. Wang, (1996) The min-max function differentiation and training of fuzzy neural networks, IEEE trans. Neural Networks 7(5):1139-1150