Istituto di Scienza e Tecnologie dell'Informazione     
Caiafa C. F., Salerno E., Proto A. N. Blind source separation applied to spectral unmixing: comparing different measures of nongaussianity. In: Knowledge-Based Intelligent Information and Engineering Systems. 11th International Conference KES 2007, XVII Italian Workshop on Neural Networks (Vietri sul Mare, 12-14 September 2007). Proceedings, vol. Part III pp. 1 - 8. Bruno Apolloni, Robert J. Howlett and Lakhmi Jain (eds.). (Lecture Notes in Artificial Intelligence, vol. 4694). Springer, 2007.
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 charcterize remote-sensed images.
URL: http://www.springerlink.com/content/n67vj4lg6112/?p=99ee994f2ad048d88b0eee64dd8e3e2a&pi=54
DOI: 10.1007/978-3-540-74829-8
Subject Blind spectral unmixing
Dependent component analysis
Measures of nongaussianity
Hyperspectral images
Unsupervised classification
J.2 Physical Sciences and Engineering
I.4.8 Scene analysis

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