Singular spectrum analysis for effective feature extraction in hyperspectral imaging

Research output: Contribution to journalArticle

49 Citations (Scopus)

Abstract

As a very recent technique for time series analysis, Singular Spectrum Analysis (SSA) has been applied in many diverse areas, where an original 1D signal can be decomposed into a sum of components including varying trends, oscillations and noise. Considering pixel based spectral profiles as 1D signals, in this paper, SSA has been applied in Hyperspectral Imaging (HSI) for effective feature extraction. By removing noisy components in extracting the features, the discriminating ability of the features has been much improved. Experiments show that this SSA approach supersedes the Empirical Mode Decomposition (EMD) technique from which our work was originally inspired, where improved results in effective data classification using Support Vector Machine (SVM) are also reported.
LanguageEnglish
Pages1886-1890
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Volume11
Issue number11
Early online date14 Apr 2014
DOIs
Publication statusPublished - Nov 2014

Fingerprint

Spectrum analysis
Feature extraction
Time series analysis
time series analysis
Support vector machines
pixel
Pixels
oscillation
decomposition
Decomposition
Hyperspectral imaging
analysis
experiment
Experiments
support vector machine
trend

Keywords

  • singular spectrum analysis
  • hyperspectral imaging
  • feature extraction
  • data classification
  • support vector machine

Cite this

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Singular spectrum analysis for effective feature extraction in hyperspectral imaging. / Zabalza, Jaime; Ren, Jinchang; Wang, Zheng; Marshall, Stephen; Wang, Jun.

In: IEEE Geoscience and Remote Sensing Letters, Vol. 11, No. 11, 11.2014, p. 1886-1890.

Research output: Contribution to journalArticle

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