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Unsupervised Hyperspectral Band Selection Using Spectral-Spatial Iterative Greedy Algorithm.

Xin Yang1, Wenhong Wang1

  • 1College of Computer Science, Liaocheng University, Liaocheng 252000, China.

Sensors (Basel, Switzerland)
|September 27, 2025
PubMed
Summary
This summary is machine-generated.

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Hyperspectral band selection (BS) is improved by the new Spectral-Spatial Iterative Greedy Algorithm (SSIGA). This method effectively uses spatial and spectral information for better data dimensionality reduction in hyperspectral remote sensing images (HSIs).

Area of Science:

  • Remote Sensing
  • Computer Vision
  • Data Science

Background:

  • Hyperspectral band selection (BS) is crucial for reducing data dimensionality in hyperspectral remote sensing images (HSIs).
  • Existing searching-based BS methods often fail to fully leverage inherent spatial and spectral information, limiting their effectiveness.
  • There is a need for unsupervised BS methods that integrate both spatial and spectral prior information.

Purpose of the Study:

  • To propose a novel unsupervised band selection method, the Spectral-Spatial Iterative Greedy Algorithm (SSIGA).
  • To address the limitations of existing methods by effectively exploiting spatial and spectral prior information in HSIs.
  • To improve the performance of BS for classification applications of HSIs.

Main Methods:

Keywords:
band selectionhyperspectral remote sensing imagesiterative greedy algorithmlocal searchspectral–spatial information

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  • SSIGA employs K-means clustering with balanced cluster size constraints for spectral information processing.
  • A K-nearest neighbor graph is constructed for each cluster to facilitate efficient local search.
  • An objective function evaluates band discriminability and redundancy using Fisher score, superpixel segmentation, information entropy, and mutual information.

    • Main Results:

    • SSIGA demonstrates superior performance compared to state-of-the-art methods on three real HSI datasets.
    • The algorithm effectively utilizes spatial and spectral information for band selection.
    • Experimental results validate the efficacy of the proposed objective function and local search strategy.

    Conclusions:

    • The proposed SSIGA is an effective unsupervised band selection method for HSIs.
    • SSIGA achieves superior performance by integrating spectral and spatial information.
    • The method offers a promising approach for dimensionality reduction in hyperspectral image classification.