Document - Blind source separation applied to spectral unmixing: comparing different measures of nongaussianity

2007

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Blind source separation applied to spectral unmixing: comparing different measures of nongaussianity

Caiafa C. F., Salerno E., Proto A. N.

Blind spectral unmixing Dependent component analysis Measures of nongaussianity Hyperspectral images Unsupervised classification

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.

Source: Knowledge-Based Intelligent Information and Engineering Systems. 11th International Conference KES 2007, XVII Italian Workshop on Neural Networks, pp. 1–8, Vietri sul Mare, Italy, 12-14 September 2007

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@inproceedings{oai:it.cnr:prodotti:43977,
	title = {Blind source separation applied to spectral unmixing: comparing different measures of nongaussianity},
	author = {Caiafa C.  F. and Salerno E. and Proto A.  N.},
	doi = {10.1007/978-3-540-74829-8},
	booktitle = {Knowledge-Based Intelligent Information and Engineering Systems. 11th International Conference KES 2007, XVII Italian Workshop on Neural Networks, pp. 1–8, Vietri sul Mare, Italy, 12-14 September 2007},
	year = {2007}
}