Singular spectrum analysis for effective noise removal and improved data classification in hyperspectral imaging

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Zabalza, Jaime and Ren, Jinchang and Marshall, Stephen; (2017) Singular spectrum analysis for effective noise removal and improved data classification in hyperspectral imaging. In: 2014 6th Workshop on Hyperspectral Image and Signal Processing. IEEE, CHE. ISBN 9781467390125 (https://doi.org/10.1109/WHISPERS.2014.8077583)

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Abstract

Based on the well-known Singular Value Decomposition (SVD), Singular Spectrum Analysis (SSA) has been widely employed for time series analysis and forecasting in decomposing the original series into a sum of components. As such, each 1-D signal can be represented with varying trend, oscillations and noise for easy enhancement of the signal. Taking each spectral signature in Hyperspectral Imaging (HSI) as a 1-D signal, SSA has been successfully applied for signal decomposition and noise removal whilst preserving the discriminating power of the spectral profile. Two well-known remote sensing datasets for land cover analysis, AVIRIS 92AV3C and Salinas C, are used for performance assessment. Experimental results using Support Vector Machine (SVM) in pixel based classification have indicated that SSA has suppressed the noise in significantly improving the classification accuracy.

ORCID iDs

Zabalza, Jaime ORCID logoORCID: https://orcid.org/0000-0002-0634-1725, Ren, Jinchang ORCID logoORCID: https://orcid.org/0000-0001-6116-3194 and Marshall, Stephen ORCID logoORCID: https://orcid.org/0000-0001-7079-5628;
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