Singular spectrum analysis for image processing

Rodríguez-Aragón, Licesio J.; Zhigljavsky, Anatoly

doi:10.4310/sii.2010.v3.n3.a14

Cited by 48 publications

(29 citation statements)

References 8 publications

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“…Derived from the basic SSA algorithm, the 2D-SSA method is an extension employed for 2-D signals or images [15,16]. We already introduced and evaluated the 2D-SSA algorithm for feature extraction in HSI [9], where its conventional implementation is well-know and can be easily found in several of the cited works [9,15,16].…”

Section: Fast Implementation F-2d-ssamentioning

confidence: 99%

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Fast implementation of two-dimensional singular spectrum analysis for effective data classification in hyperspectral imaging

Zabalza

Qing

Yuen

et al. 2018

Journal of the Franklin Institute

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Section: Fast Implementation F-2d-ssamentioning

confidence: 99%

“…We already introduced and evaluated the 2D-SSA algorithm for feature extraction in HSI [9], where its conventional implementation is well-know and can be easily found in several of the cited works [9,15,16]. In the following, a brief summary is provided for clarity to the readers.…”

Section: Fast Implementation F-2d-ssamentioning

confidence: 99%

Fast implementation of two-dimensional singular spectrum analysis for effective data classification in hyperspectral imaging

Zabalza

Qing

Yuen

et al. 2018

Journal of the Franklin Institute

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“…As spatial features can also lead to improved data classification [26], we propose the introduction of 2D-SSA for HSI spatial-domain feature extraction. Although some recent work has been reported using 2D-SSA in some applications [43][44][45][46][47], applying 2D-SSA for effective feature extraction and data classification in HSI has been seldom addressed. In this section, the concept of 2D-SSA, with mathematical description and examples of application are provided for an easy understanding.…”

Section: Extension To 2d-ssa For Analyzing Hsimentioning

confidence: 99%

“…In general, for a fixed EV grouping, the implementation with a small window leads to good reconstructions of the image, as a larger window may produce too smoothed results and cause a mixing problem [47]. Also, symmetry property [41] of the trajectory matrices fixes the available implementation range in [2, N x /2] and [2, N y /2] for L x and L y , respectively, and the selection of values L x ≠L y derives in a non-symmetric smoothing of the image [46]. With respect to the EV grouping, depending on the Eigenvalues selected, the reconstruction of the image discards different components that relate to the main trends, oscillations and noise, among others.…”

Section: Feature Extractionmentioning

confidence: 99%

Novel Two-Dimensional Singular Spectrum Analysis for Effective Feature Extraction and Data Classification in Hyperspectral Imaging

Zabalza

Ren

Zheng

et al. 2015

IEEE Trans. Geosci. Remote Sensing

153

140

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Feature extraction is of high importance for effective data classification in hyperspectral imaging (HSI). Considering the high correlation among band images, spectral-domain feature extraction is widely employed. For effective spatial information extraction, a 2-D extension to singular spectrum analysis (SSA), a recent technique for generic data mining and temporal signal analysis, is proposed. With 2D-SSA applied to HSI, each band image is decomposed into varying trend, oscillations and noise. Using the trend and selected oscillations as features, the reconstructed signal, with noise highly suppressed, becomes more robust and effective for data classification. Three publicly available data sets for HSI remote sensing data classification are used in our experiments. Comprehensive results using a support vector machine (SVM) classifier have quantitatively evaluated the efficacy of the proposed approach. Benchmarked with several state-of-the-art methods including 2-D empirical mode decomposition (2D-EMD), it is found that our proposed 2D-SSA approach generates the best results in most cases. Unlike 2D-EMD which requires sequential transforms to obtain detailed decomposition, 2D-SSA extracts all components simultaneously. As a result, the executive time in feature extraction can also be dramatically reduced. The superiority in terms of enhanced discrimination ability from 2D-SSA is further validated when a relatively weak classifier, k-nearest neighbor (k-NN), is used for data classification. In addition, the combination of 2D-SSA with 1D-PCA (2D-SSA-PCA) has generated the best results among several other approaches, which has demonstrated the great potential in combining 2D-SSA with other approaches for effective spatial-spectral feature extraction and dimension reduction in HSI. Index Terms-Data classification, feature extraction, hyperspectral imaging (HSI), 2-D empirical mode decomposition (2D-EMD), 2-D singular spectrum analysis (2D-SSA).

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“…The algorithm of one dimension SSA clearly explained in original works [6,7], was used by author for the analysis and forecasting time series of pole coordinates. 2dSSA and principal component analysis are widely used in the analysis of photo and video, e. g. [8]. Applying 2dSSA to the precision analysis of ionospheric maps, most likely, is attempted here for the rst time.…”

Section: Graph Inmentioning

confidence: 99%

Formal estimation of the random component in global maps of total electron content

Choliy

2016

Adv.Astron.Spa.Physics

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Singular spectrum analysis for image processing

Cited by 48 publications

References 8 publications

Fast implementation of two-dimensional singular spectrum analysis for effective data classification in hyperspectral imaging

Fast implementation of two-dimensional singular spectrum analysis for effective data classification in hyperspectral imaging

Novel Two-Dimensional Singular Spectrum Analysis for Effective Feature Extraction and Data Classification in Hyperspectral Imaging

Formal estimation of the random component in global maps of total electron content

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