Effective feature extraction and data reduction with hyperspectral imaging in remote sensing

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Although PCA has been widely used for feature extraction and data reduction, it suffers from three main drawbacks: high computational cost, large memory requirement and low efficacy in processing large datasets such as HSI. This column analysed two variations of PCA, namely SPCA and Seg-PCA. Seg-PCA can further improve classification accuracy whilst significantly reducing the computational cost and memory requirement, without requiring prior knowledge. There is potential to apply similar feature extraction and data reduction techniques in application areas beyond HSI when analysis of large dimensional datasets is required such as magnetic resonance imaging (MRI) and digital video processing.
Original languageEnglish
Pages (from-to)149-154
Number of pages6
JournalIEEE Signal Processing Magazine
Issue number4
Publication statusPublished - Jul 2014


  • hyperspectral Imaging
  • feature extraction
  • data reduction
  • remote sensing
  • covariance matrices
  • hypercubes
  • memory management
  • principal component analysis

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  • Prizes

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

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

    Prize: Prize (including medals and awards)


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