Self-Organizing Map for Hyperspectral Image Analysis

1. Greeen, R.O., Editor, AVIRIS Earth Science Workshop Proceedings, 1988-2000. Available at http://makalu.jpl.nasa.gov/

2. Ifarraguerri, A.; Chang, C.-I. “Multispectral and Hyperspectral Image Analysis with Convex Cones”. IEEE Trans. Geoscience and Remote Sensing, Vol. 37 Issue 2 Part 1, March 1999, 756–770.

   Article  Google Scholar 

3. Jimenez, L.O., Morales-Morell, A., Creus, A., “Classification of Hyperdimensional Data Based on Feature and Decision Fusion Approaches Using Projection Pursuit, Majority Voting, and Neural Networks”, IEEE Trans. Geoscience and Remote Sensing, Vol. 37 Issue 3 Part 1, May 1999,. 1360–1366.

   Article  Google Scholar 

4. Richardson L and Kruse F.A., “Identification and Classification of mixed phytoplankton assemblages using AVIRIS image derived spectra” Summaries of the VIII JPL Airborne Earth Science Workshop. (1999), 339–347

   Google Scholar 

5. Parra L, Spence C., Sadja P, Ziehe A., Müller K-R, “Unmixing Hypersepectral Data”Proc. Advances in Neural Information Processing System 12, Proc. Of the 1999 Conference, MIT Press (1999) 942–948

   Google Scholar 

6. Kohonen, T., Self-Organizing Maps (2nd ed.), Springer Series in Information Science. (1997)

   Google Scholar 

7. Bruske, J and Merényi, E.: “Estimating the Intrinsic Dimensionality of Hyperspectral Images”. Proc. European Symposium on Artificial Neural Network, ESANN’99, Bruges, Belgium, (1999), 105–110.

   Google Scholar 

8. Taudjin S and Landgrebe D., Classification of High Dimensional Data with Limited Training Samples, Doctoral Thesis, School of Elelectrical Engineering and Computer Science, Purdue University. (1998).

   Google Scholar 

9. Martínez P., Pérez R.M., Aguilar P.L., Bachiller, P. and Diaz, P., “A Neuronal Tool for AVIRIS Hyperspectral Unmixing”, Summaries of the VIII JPL Airborne Earth Science Workshop, JPL/NASA, (1999), 281–286.

   Google Scholar 

10. Aguilar, P.L., Pérez, R.M., Martínez, P., Bachiller, P., Merchán, A., “Spectra Evaluation and Recognition in the Mixture Problem Using SOFM Algorithm”, Proc. International Symposium on Engineering of Intelligent Systems, (EIS’98), Vol. 2, (1998), 118–124.

    Google Scholar 

11. Aguilar, P.L., Plaza, A., Martínez, P., Pérez, R.M., “Endmember Extraction by a Self-Organizing Neural Network on Hyperspectral Images”, Proc. International Conference on Automation, Robotics and Computer Vision, Nanyang Technological Institute, Singapore, (2000).

    Google Scholar 

12. Aguilar P.L, Martínez, P., Pérez R.M., Hormigo, A., “Abundance Extractions from AVIRIS Images Using a Self Organizing Neural Network”, Summaries of the IX JPL Airborne Earth Science Workshop, JPL/NASA (2000), 281–286,.

    Google Scholar 

13. Aguilar P.L., “Cuantificación de Firmas Hiperespectrales Usando Mapas Autoorganizativos”, Ph. D. Thesis, Escuela Politécnica, Universidad de Extremadura, (Chapter 5), 2000.

    Google Scholar 

14. Chuvieco E., Elementos de Teledetección Espacial, Ed Rialp (2000)

    Google Scholar 

15. Ultsch, A. and Siemon, H.P., “Kohonen’s Self Organizing Feature Maps for Exploratory Data Analysis”, Proc. ICNN’90 International Neural Network Conference, (1990), 305–308.

    Google Scholar 

16. Antonille, S. and Gualtieri, J.A., “Visualizing Clusters in High-Dimensional Data with a Kohonen Self Organizing Map”. Summaries of the IX JPL Airborne Earth Science Workshop, JPL/NASA (2000) 281–286.

    Google Scholar 

17. Martínez, P, Gualtieri, J.A., Aguilar, P.L., Pérez R, Linaje M, Preciado J.C., Plaza A., “Hyperspectral image classification using self-organizing map”, Summaries of the X JPL Airborne Earth Science Workshop, in press, JPL/NASA, 2001.

    Google Scholar 

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