Spectral dimensionality reduction based on intergrated bispectrum phase for hyperspectral image analysis

Saipullah, Khairul Muzzammil (2011) Spectral dimensionality reduction based on intergrated bispectrum phase for hyperspectral image analysis. In: Image and Signal Processing for Remote Sensing XVII, Monday 19 September 2011, Prague, Czech Republic.

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Abstract

In this paper, we propose a method to reduce spectral dimension based on the phase of integrated bispectrum. Because of the excellent and robust information extracted from the bispectrum, the proposed method can achieve high spectral classification accuracy even with low dimensional feature. The classification accuracy of bispectrum with one dimensional feature is 98.8%, whereas those of principle component analysis (PCA) and independent component analysis (ICA) are 41.2% and 63.9%, respectively. The unsupervised segmentation accuracy of bispectrum is also 20% and 40% greater than those of PCA and ICA, respectively.

Item Type:	Conference or Workshop Item (Paper)
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Faculty of Electronics and Computer Engineering > Department of Computer Engineering
Depositing User:	Engr. Khairul Muzzammil Saipullah
Date Deposited:	11 Jul 2012 00:05
Last Modified:	28 May 2015 02:39
URI:	http://eprints.utem.edu.my/id/eprint/4097
Statistic Details:	View Download Statistic

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