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

Zabalza, Jaime; Qing, Chunmei; Yuen, Peter W. T.; Sun, Guifan; Zhao, Huimin; Ren, Jinchang

doi:10.1016/j.jfranklin.2017.05.020

Cited by 27 publications

(17 citation statements)

References 25 publications

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“…Singular Spectrum Analysis (SSA) can be used to decompose a 1-D signal into low-frequency components of the trend, oscillations, and noise [19]. Recently, 2D-SSA was found effective for smoothing images and feature extraction in hyperspectral images [19] [15].…”

Section: B Conventional 2d-ssa Analysismentioning

confidence: 99%

A New Cost Function for Spatial Image Steganography Based on 2D-SSA and WMF

et al. 2021

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As an essential tool for secure communications, adaptive steganography aims to communicate secret information with the least security cost. Inspired by the Ranking Priority Profile (RPP), we propose a novel two-step cost function for adaptive steganography in this paper. The RPP mainly includes three rules, i.e. Complexity-First rule, the Clustering rule and the Spreading rule, to design a cost function. We use the two-dimensional Singular Spectrum Analysis (2D-SSA) and Weighted Median Filter (WMF) in designing the two-step cost function. The 2D-SSA is employed in selecting the key components and clustering the embedding positions, which follows the Complexity-First rule and the Clustering rule. Also, we deploy the Spreading rule to smooth the resulting image produced by 2D-SSA with WMF. Extensive experiments have shown the efficacy of the proposed method, which has improved performance over four benchmarking approaches against non-shared selection channel attack. It also provides comparable performance in selection-channel-aware scenarios, where the best results are observed when the relative payload is 0.3 bpp or larger. Besides, the proposed approach is much faster than other model-based methods.INDEX TERMS Image steganography; feature extraction; singular spectrum analysis (SSA); Weighted median filtering (WMF); ranking priority profile.

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Section: B Conventional 2d-ssa Analysismentioning

confidence: 99%

A New Cost Function for Spatial Image Steganography Based on 2D-SSA and WMF

et al. 2021

Self Cite

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“…SSA is a data-driven method for decomposing nonlinear time series into components with certain interpretations such as trend, oscillation or noise. SSA method has been utilized in fields of GPS data processing [27], hyperspectral imaging [31] and economics [32]. Recently, we also apply this method to improve absolute ranging precision of objects with uniform motion [33].…”

Section: Introductionmentioning

confidence: 99%

Singular Spectrum Analysis for Extracting Low Amplitude Vibrations in Femtosecond Laser Time-of-Flight Distance Measurements

Cao

Song

et al. 2021

IEEE Photonics J.

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We utilize singular spectrum analysis (SSA) for post-processing distance measurement data obtained by a dual-comb femtosecond laser rangefinder. Signal denoising technique based on SSA allows detection of low amplitude vibrations from time series of distance data with limited signalto-noise ratio (SNR). Various one-dimensional vibrations, including harmonic, amplitude modulated harmonic, and sinc-function vibrations, with micrometer amplitude comparable with measurement uncertainty of laser rangefinder, have been imposed on the target mirror by a piezo positioner. These vibrations have been extracted by SSA and the obtained vibration amplitudes agree with that imposed on the target very well. A commercial interferometer is used to evaluate the accuracy of SSA. Standard deviations of residuals between the SSA extractions and interferometer measurements are smaller than 0.6 µm. This technique is promising for complex moving pattern detection in object tracking and remote sensing.

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“…Its main aim is to decompose the original time series into a set of components that can be interpreted as trend components, seasonal components, and noise components [ 3 , 4 , 5 , 6 ]. SSA has proven both wide usefulness and applicability across many applications [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ], being that its scope of application ranges from parameter estimation to time series filtering, synchronization analysis, and forecasting [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

The Decomposition and Forecasting of Mutual Investment Funds Using Singular Spectrum Analysis

Rodrigues

Pimentel

Messala

et al. 2020

Entropy

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Singular spectrum analysis (SSA) is a non-parametric method that breaks down a time series into a set of components that can be interpreted and grouped as trend, periodicity, and noise, emphasizing the separability of the underlying components and separate periodicities that occur at different time scales. The original time series can be recovered by summing all components. However, only the components associated to the signal should be considered for the reconstruction of the noise-free time series and to conduct forecasts. When the time series data has the presence of outliers, SSA and other classic parametric and non-parametric methods might result in misleading conclusions and robust methodologies should be used. In this paper we consider the use of two robust SSA algorithms for model fit and one for model forecasting. The classic SSA model, the robust SSA alternatives, and the autoregressive integrated moving average (ARIMA) model are compared in terms of computational time and accuracy for model fit and model forecast, using a simulation example and time series data from the quotas and returns of six mutual investment funds. When outliers are present in the data, the simulation study shows that the robust SSA algorithms outperform the classical ARIMA and SSA models.

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

Cited by 27 publications

References 25 publications

A New Cost Function for Spatial Image Steganography Based on 2D-SSA and WMF

A New Cost Function for Spatial Image Steganography Based on 2D-SSA and WMF

Singular Spectrum Analysis for Extracting Low Amplitude Vibrations in Femtosecond Laser Time-of-Flight Distance Measurements

The Decomposition and Forecasting of Mutual Investment Funds Using Singular Spectrum Analysis

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