Abstract
We report some of our results of a particular blind source separation technique applied to spectral unmixing of remote-sensed hyperspectral images. Different nongaussianity measures are introduced in the learning procedure, and the results are compared to assess their relative efficiencies, with respect to both the output signal-to-interference ratio and the overall computational complexity. This study has been conducted on both simulated and real data sets, and the first results show that skewness is a powerful and unexpensive tool to extract the typical sources that characterize remote-sensed images.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Keshava, N., Mustard, J.: Spectral unmixing. IEEE Signal Process. Mag. 19(1), 44–57 (2002)
Berman, M., Kiiveri, H., Lagerstrom, R., Ernst, A., Dunne, R., Huntington, J.: ICE: A statistical approach to identifying endmembers. IEEE Transactions on Geoscience and Remote Sensing 42, 2085–2095 (2004)
Nascimento, J.M.P., Bioucas Dias, J.M.: Does Independent Component Analysis Play a Role in Unmixing Hyperspectral Data? IEEE Transactions on Geoscience and Remote Sensing 43, 175–187 (2005)
Caiafa, C.F., Proto, A.N.: Separation of statistically dependent sources using an L 2-distance non-Gaussianity measure. Signal Processing 86, 3404–3420 (2006)
Caiafa, C.F., Salerno, E., Proto, A.N., Fiumi, L.: Dependent Component Analysis as a Tool for Blind Spectral Unmixing of Remote Sensed Images. In: Proc. EUSIPCO 2006, Florence, Italy, September 4-8, 2006, pp. 4–8 (2006)
Caiafa, C.F., Salerno, E., Proto, A.N., Fiumi, L.: Blind Spectral Unmixing by Local Maximization of Non-Gaussianity. Signal Processing (submitted)
Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. J. Wiley & Sons, New York (2001)
Cruces, S., Cichocki, A., Amari, S.-I.: The Minimum Entropy and Cumulants Based Contrast Functions for Blind Source Extraction. In: Mira, J.M., Prieto, A.G. (eds.) IWANN 2001. LNCS, vol. 2085, pp. 13–15. Springer, Heidelberg (2001)
Parzen, E.: On estimation of a probability density function and mode. Annals of Mathematical Statistics 33, 1065–1076 (1962)
Silverman, B.W.: Density Estimation for Statistics and Data Analysis. Chapman and Hall, New York (1985)
Erdogmus, D., Hild, K.E., Principe, J.C., Lazaro, M., Santamaria, I.: Adaptive blind deconvolution of linear channels using Renyi’s entropy with Parzen window estimation. IEEE Transactions on Signal Processing 52, 1489–1498 (2004)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Caiafa, C.F., Salerno, E., Proto, A.N. (2007). Blind Source Separation Applied to Spectral Unmixing: Comparing Different Measures of Nongaussianity. In: Apolloni, B., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2007. Lecture Notes in Computer Science(), vol 4694. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74829-8_1
Download citation
DOI: https://doi.org/10.1007/978-3-540-74829-8_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74828-1
Online ISBN: 978-3-540-74829-8
eBook Packages: Computer ScienceComputer Science (R0)