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

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

2 Citations (Scopus)
6 Downloads (Pure)

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.
Original languageEnglish
Title of host publication2014 6th Workshop on Hyperspectral Image and Signal Processing
Subtitle of host publicationEvolution in Remote Sensing (WHISPERS)
Place of PublicationPiscataway, NJ
PublisherIEEE
Number of pages4
ISBN (Electronic)9781467390125
DOIs
Publication statusPublished - 26 Oct 2017
Event 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) - Lausanne, Switzerland
Duration: 24 Jun 201427 Jun 2014

Conference

Conference 2014 6th Workshop on Hyperspectral Image and Signal Processing
CountrySwitzerland
CityLausanne
Period24/06/1427/06/14

Fingerprint

Spectrum analysis
Time series analysis
Singular value decomposition
Support vector machines
Remote sensing
Pixels
Hyperspectral imaging

Keywords

  • hyperspectral imaging
  • feature extraction
  • spectral analysis
  • support vector machines
  • image reconstruction
  • matrix decomposition

Cite this

Zabalza, J., Ren, J., & Marshall, S. (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: Evolution in Remote Sensing (WHISPERS) Piscataway, NJ: IEEE. https://doi.org/10.1109/WHISPERS.2014.8077583
Zabalza, Jaime ; Ren, Jinchang ; Marshall, Stephen. / Singular spectrum analysis for effective noise removal and improved data classification in hyperspectral imaging. 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). Piscataway, NJ : IEEE, 2017.
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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.",
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Zabalza, J, Ren, J & Marshall, S 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: Evolution in Remote Sensing (WHISPERS). IEEE, Piscataway, NJ, 2014 6th Workshop on Hyperspectral Image and Signal Processing, Lausanne, Switzerland, 24/06/14. https://doi.org/10.1109/WHISPERS.2014.8077583

Singular spectrum analysis for effective noise removal and improved data classification in hyperspectral imaging. / Zabalza, Jaime; Ren, Jinchang; Marshall, Stephen.

2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). Piscataway, NJ : IEEE, 2017.

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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N1 - © 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.

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N2 - 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.

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Zabalza J, Ren J, Marshall S. Singular spectrum analysis for effective noise removal and improved data classification in hyperspectral imaging. In 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). Piscataway, NJ: IEEE. 2017 https://doi.org/10.1109/WHISPERS.2014.8077583