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
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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.
Creators(s): |
Zabalza, Jaime ![]() ![]() ![]() | Item type: | Book Section |
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ID code: | 65765 |
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: | 01 Jan 2021 07:10 |
Related URLs: | |
URI: | https://strathprints.strath.ac.uk/id/eprint/65765 |
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