Singular Spectrum Analysis for effective noise removal and improved data classification in Hyperspectral Imaging

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.

[1]  Begüm Demir,et al.  Empirical Mode Decomposition of Hyperspectral Images for Support Vector Machine Classification , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[2]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[3]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[4]  Junwei Han,et al.  Novel Folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing , 2014 .

[5]  Stephen Marshall,et al.  Quality-assured fingerprint image enhancement and extraction using hyperspectral imaging , 2011, ICDP.

[6]  Jinchang Ren,et al.  Embedded SVM on TMS320C6713 for signal prediction in classification and regression applications , 2012, 2012 5th European DSP Education and Research Conference (EDERC).

[7]  Stephen Marshall,et al.  Quantitative assessment of beef quality with hyperspectral imaging using machine learning techniques , 2012 .

[8]  Jun Wang,et al.  Fast Implementation of Singular Spectrum Analysis for Effective Feature Extraction in Hyperspectral Imaging , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[9]  Stephen Marshall,et al.  Effective Feature Extraction and Data Reduction in Remote Sensing Using Hyperspectral Imaging [Applications Corner] , 2014, IEEE Signal Processing Magazine.

[10]  Rick Archibald,et al.  Feature Selection and Classification of Hyperspectral Images With Support Vector Machines , 2007, IEEE Geoscience and Remote Sensing Letters.