Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 11, Issue 11, Pages 1886-1890Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2014.2312754
Keywords
Data classification; feature extraction; hyperspectral imaging (HSI); singular spectrum analysis (SSA); support vector machine (SVM)
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Funding
- National Science Foundation of China [61202165, 61003201]
- Royal Society of Edinburgh [6121130125]
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As a very recent technique for time-series analysis, singular spectrum analysis (SSA) has been applied in many diverse areas, where an original 1-D signal can be decomposed into a sum of components, including varying trends, oscillations, and noise. Considering pixel-based spectral profiles as 1-D signals, in this letter, SSA has been applied in hyperspectral imaging 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 technique from which our work was originally inspired, where improved results in effective data classification using support vector machine are also reported.
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