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Feature Selection Based on High Dimensional Model Representation for Hyperspectral Images.

Gulsen Taskin, Huseyin Kaya, Lorenzo Bruzzone

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |March 31, 2017
    PubMed
    Summary
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    This study introduces a new feature selection algorithm for hyperspectral image analysis to combat the curse of dimensionality. The proposed method achieves high classification accuracy and robust feature selection with efficient computation.

    Area of Science:

    • Remote Sensing
    • Computer Vision
    • Data Science

    Background:

    • Hyperspectral image analysis faces challenges in classification due to high spectral feature correlation and noise.
    • The curse of dimensionality (Hughes effect) significantly degrades classification accuracy with limited training data.
    • Existing methods often address dimensionality reduction and classification jointly, impacting performance.

    Purpose of the Study:

    • To propose a novel feature-selection algorithm for dimensionality reduction in hyperspectral imaging.
    • To evaluate the proposed algorithm's effectiveness against conventional methods.
    • To improve classification accuracy and feature stability in high-dimensional spectral data.

    Main Methods:

    • Development of a new feature-selection algorithm based on High Dimensional Model Representation (HDMR).

    Related Experiment Videos

  • Testing the algorithm on synthetic datasets and real-world hyperspectral data.
  • Comparison with conventional feature-selection techniques based on accuracy, stability, and computational time.

    • Main Results:

    • The proposed HDMR-based algorithm demonstrates superior classification accuracy compared to conventional methods.
    • Selected features exhibit enhanced stability, indicating robustness.
    • The algorithm achieves satisfactory computational efficiency.

    Conclusions:

    • The novel feature-selection approach effectively addresses the curse of dimensionality in hyperspectral image analysis.
    • The method offers a promising solution for improving classification performance and reliability.
    • The algorithm provides a balance between accuracy, feature robustness, and computational cost.