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

Jaime Zabalza, Chunmei Qing, Peter Yuen, Genyun Sun, Huimin Zhao, Jinchang Ren

Research output: Contribution to journalArticle

3 Citations (Scopus)
19 Downloads (Pure)

Abstract

Although singular spectrum analysis (SSA) has been successfully applied for data classification in hyperspectral remote sensing, it suffers from extremely high computational cost, especially for 2D-SSA. As a result, a fast implementation of 2D-SSA namely F-2D-SSA is presented in this paper, where the computational complexity has been significantly reduced with a rate up to 60%. From comprehensive experiments undertaken, the effectiveness of F-2D-SSA is validated producing a similar high-level of accuracy in pixel classification using support vector machine (SVM) classifier, yet with a much reduced complexity in comparison to conventional 2D-SSA. Therefore, the introduction and evaluation of F-2D-SSA completes a series of studies focused on SSA, where in this particular research, the reduction in computational complexity leads to potential applications in mobile and embedded devices such as airborne or satellite platforms.
Original languageEnglish
Number of pages19
JournalJournal of the Franklin Institute
Early online date12 May 2017
DOIs
Publication statusE-pub ahead of print - 12 May 2017

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Keywords

  • data classification
  • fast 2-D singular spectrum analysis
  • hyperspectral imaging
  • land cover analysis
  • remote sensing

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