Automated classification of visible and near-infrared spectra using self-organizing maps

IEEE Aerospace Conference Proceedings

Volumn , Issue , 2007, Pages

  Roush, Ted L ^a   Hogan, Robert ^a  

^a NASA AMES RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTER ANALYSIS; PARTICLE SIZE; ROBUSTNESS (CONTROL SYSTEMS); SATELLITES; SPACE RESEARCH; STATISTICAL METHODS;

GROSSEST DIFFERENCES; NEAR-INFRARED SPECTRA; SPACE MISSIONS;

SELF ORGANIZING MAPS;

EID: 34548762434     PISSN: 1095323X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/AERO.2007.352701     Document Type: Conference Paper

References (26)

1. Villmann T., E. Merenyi, and B. Hammer, Neural maps in remote sensing image analysis, Neural Networks, 16, 389-403, 2003.
2. TES web-site counter, /tes.asu.edu/webdata/counts.html
3. Castaño R. L., M. Judd, R.C. Anderson, R. C., and T. Estlin T., Machine learning challenges in Mars rover traverse science, Intl. Conf. Machine Learning '03, Washington, DC, 2003
4. Roush, T.L., M. Shipman, R. Morris, P. Gazis, and L. Pedersen, Essential autonomous science inference on rovers (EASIR), Proceedings IEEE 2004 Aerospace Conference, Big Sky, Montana, 2004.
5. Howell E. S., E. Merenyi, and L.A. Lebofsky, Classification of asteroid spectra using a neural network, Journal of Geophysical Research -Planets, 99, 10847-10865, 1994.
6. Merenyi, E., R.B. Singer, and J.S. Miller, Mapping of spectral variations on the surface of mars from high spectral resolution telescopic images. Icarus, 124, 280-295, 1996.
7. Gilmore M.S., R.L., Castaño, T. Mann, R.C. Anderson, R. C., E.D. Mjolsness, R. Manduchi and S.R. Saunders, Strategies for autonomous rovers at Mars, Journal of Geophysical Research -Planets, 105, 29223-29238, 2000.
8. Gilmore M.S., M.D. Merrill, R., Castaño, B. Bornstein, and J.P. Greenwood, Effects of mars analogue deposition on the automated detection of calcite in the visible/near-infrared spectra, Icarus, 172, 641-646, 2004.
9. Hogan, R. and T.L. Roush, Description and evaluation of an automated classification scheme based upon self-organizing maps trained with mineral spectra, Neural Networks, in revision, 2006
10. Kaski S., J. Kangas, T. Kohonen, Bibliography of Self-Organizing Map (SOM) Papers: 1981-1997, Neural Computing Surveys, 1: 102-350 Available electronicly at http://www.icsi.berkeley.edu/~jagota/NCS/, 1998.
11. Abonyi, J., S. Nemeth, C. Vincze, and P. Arva, Process Analysis and product quality estimation by self-organizing maps with an application to polyethylene production, Computers in Industry, 52, 221-223, 2003.
12. Hiirsalmi, M., Abnormality detection using SOM modeling, MODUS-project Wastewater Case Study, Research Report, TTE1-2001-16, 2001.
13. Kukkonen, S., Kalviainen, and J. Parkkinen, Visual inspection by spectral features in ceramics industry, Part of the EUROPTO Conference on Polarization and Color Techniques in Industrial Inspection, Munich, Germany SPIE, 3826, 64-75 1999.
14. Kukkonen S. and J. Parkkinen, Color features for quality control in ceramic tile industry, Optical Engineering, 40, 170-177, 2001.
15. Tokutaka H., K. Yoshihara, K. Fujimara, K. Iwamota, and K. Obu-Cann, Application of Self-organizing Maps (SOM) to Auger Electron Spectroscopy (AES), Surface Interface Analysis,27, 783-788, 2000.
16. Yang H., I.R. Lewis, and P.R. Griffiths, Raman spectrometry and neural networks for the classification of wood types. 2. Kohonen self-organizing maps, Spectrochimica Acta Part A 55, 2783-2791, 1999
17. Yacoub M., F. Badran, and S. Thiria, A Topological Hierarchical Clustering: Application to Ocean Color Classification, ICANN 2001, LNCS 2130, Berlin Heidelberg:Springer-Verlag, pp 492-499, 2001.
18. Haute-Kasari, M., K. Miyazawa, S. Toyooka, and J. Parkkinen, Spectral measurements of two-dimensional color images, Part of the OSJ/SPIE Conference on Optical Engineering for Sensing and Nanotechnology (ICOSN '99) Yokohama, Japan, SPIE 3740. 204-207, 1999.
19. Zhang, X., and L. Yanda, Self-Organizing Map as a New Method for Clustering and Data Analysis, in Proceedings of 1993 International Joint Conference on Neural Networks, 2448-2451, 1993.
20. Brosse, S., J.L. Giraudel, and S. Lek, Utilisation of non-supervised neural networks and principal component analysis to study fish assemblages. Ecological Modeling, 146, 159-166, 2001.
21. [21 ] Vasanto, J. and E. Alhoniemi, Clustering of the Self-Organizing Map, IEEE Transactions on Neural Networks, 11, 586-600, 2000.
22. Davies, D.L., and D.W. Bouldin, A Cluster Separation Measure, IEEE Trans. Pattern Analysis and Machine Intelligence, 1, 224-227, 1979.
23. Pieters, C.M., and T. Hiroi, RELAB (Reflectance Experiment LABoratory): A NASA multiuser spectroscopy facility, 35th Lunar Planetary Science Conf.. abstract no. 1720, Lunar Planetary Institute, Houston, TX.
24. ASTER spectral library, http://speclib.jpl.nasa.gov/
25. Gaffey, S.J., L.A. McFadden, D. Nash, and C.M. Pieters, Ultraviolet, visible, and near-infrared reflectance spectroscopy: Laboratory spectra of geologic materials, in Remote Geochimical Analysis: Elemental and Mineralogical Composition, C.M. Pieters and P.A.J. Englert, Eds., Cambridge Press, New York, USA, 43-77, 1993.
26. Kohavi and Provost, Machine Learning, 30, 271-274, 1998