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Dimensionality Reduction Using Band Correlation and Variance Measure from Discrete Wavelet Transformed Hyperspectral Imagery

Author

Listed:
  • Arati Paul

    (ISRO)

  • Nabendu Chaki

    (University of Calcutta)

Abstract

Contiguous narrow bands of hyperspectral images greatly increase computational complexity. Redundancy reduction is therefore necessary. Here, a minimum redundancy and maximum variance based unsupervised band selection methodology is proposed. Discrete wavelet transformation is applied on the data to reduce spatial redundancy without much effecting the overall band correlations. This in turn made the process more time efficient and noise resilient. Highly correlated bands are considered similar, and one with higher variance is accepted as being more discriminating. Finally, classification is performed with the selected bands and overall accuracy (OA) is calculated. The proposed method is compared with four other existing state-of-the-art methods in the similar field in terms of OA and execution time for evaluating the performance.

Suggested Citation

  • Arati Paul & Nabendu Chaki, 2021. "Dimensionality Reduction Using Band Correlation and Variance Measure from Discrete Wavelet Transformed Hyperspectral Imagery," Annals of Data Science, Springer, vol. 8(2), pages 261-274, June.
    • Handle: RePEc:spr:aodasc:v:8:y:2021:i:2:d:10.1007_s40745-019-00210-x
    DOI: 10.1007/s40745-019-00210-x
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