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

Khairul Muzzammil Saipullah; Deok-Hwan Kim

doi:10.1117/12.899274

26 October 2011 Spectral dimensionality reduction based on intergrated bispectrum phase for hyperspectral image analysis

Khairul Muzzammil Saipullah, Deok-Hwan Kim

Author Affiliations +

Proceedings Volume 8180, Image and Signal Processing for Remote Sensing XVII; 81801H (2011) https://doi.org/10.1117/12.899274
Event: SPIE Remote Sensing, 2011, Prague, Czech Republic

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.

Citation Download Citation

Khairul Muzzammil Saipullah and Deok-Hwan Kim "Spectral dimensionality reduction based on intergrated bispectrum phase for hyperspectral image analysis", Proc. SPIE 8180, Image and Signal Processing for Remote Sensing XVII, 81801H (26 October 2011); https://doi.org/10.1117/12.899274

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