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

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;
  • Item type:Book Section
    ID code:65765
    Dates:
    Date
    Event
    26 October 2017
    Published
    2 April 2014
    Accepted
    Notes:© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Keywords:hyperspectral imaging, feature extraction, spectral analysis, support vector machines, image reconstruction, matrix decomposition, Electrical Engineering. Electronics Nuclear Engineering, Electrical and Electronic Engineering
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Strategic Research Themes > Measurement Science and Enabling Technologies
    Depositing user: Pure Administrator
    Date deposited:12 Oct 2018 13:22
    Last modified:03 Jun 2023 01:55
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/65765
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