Novel two dimensional singular spectrum analysis for effective feature extraction and data classification in hyperspectral imaging

Jaime Zabalza, Jinchang Ren, Jiangbin Zheng, Junwei Han, Huimin Zhao, Shutao Li, Stephen Marshall

Research output: Contribution to journalArticle

75 Citations (Scopus)
304 Downloads (Pure)

Abstract

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.
Original languageEnglish
Pages (from-to)4418-4433
Number of pages16
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume53
Issue number8
Early online date20 Feb 2015
DOIs
Publication statusPublished - 31 Aug 2015

Keywords

  • data classification
  • feature extraction
  • hyperspectral imaging
  • 2-D empirical mode decomposition
  • 2-D singular spectrum analysis

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  • Projects

    Research Output

    • 75 Citations
    • 5 Article

    Dimensionality reduction based on determinantal point process and singular spectrum analysis for hyperspectral images

    Chen, W., Yang, Z., Cao, F., Yan, Y., Wang, M., Qing, C. & Cheng, Y., 5 Sep 2018, In : IET Image Processing.

    Research output: Contribution to journalArticle

    Open Access
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  • 2 Citations (Scopus)
    14 Downloads (Pure)

    Effective denoising and classification of hyperspectral images using curvelet transform and singular spectrum analysis

    Qiao, T., Ren, J., Wang, Z., Zabalza, J., Sun, M., Zhao, H., Li, S., Benediktsson, J. A., Dai, Q. & Marshall, S., 4 Jan 2017, In : IEEE Transactions on Geoscience and Remote Sensing. 55, 1, p. 119-133 15 p.

    Research output: Contribution to journalArticle

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  • 58 Citations (Scopus)
    229 Downloads (Pure)

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

    Zabalza, J., Qing, C., Yuen, P., Sun, G., Zhao, H. & Ren, J., 12 May 2017, In : Journal of the Franklin Institute. 19 p.

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  • 5 Citations (Scopus)
    22 Downloads (Pure)

    Student Theses

    Feature extraction and data reduction for hyperspectral remote sensing Earth observation

    Author: Zabalza, J., 5 Nov 2015

    Supervisor: Ren, J. (Supervisor) & Marshall, S. (Supervisor)

    Student thesis: Doctoral Thesis

    Prizes

    IET V&I Best PhD Thesis Award (only one in UK per year)

    Jaime Zabalza (Recipient), Jinchang Ren (Recipient) & Stephen Marshall (Recipient), Dec 2016

    Prize: Prize (including medals and awards)

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