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Related Experiment Video

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Efficient Clustering-based Noise Covariance Estimation for Maximum Noise Fraction.

Soumyajit Gupta1, Chandrajit Bajaj1

  • 1University of Texas at Austin, Austin TX 78705, USA.

... National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics. National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics
|December 7, 2018
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Summary

This study introduces two novel methods for estimating the Noise Covariance Matrix (NCM) to enhance hyperspectral image (HSI) classification. These techniques improve signal-to-noise ratio and spectral feature recovery, outperforming existing approaches.

Keywords:
ClassificationHyperspectral imageMaximum noise fractionNoise covariance estimationSuperpixel

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Area of Science:

  • Remote Sensing
  • Image Processing
  • Data Analysis

Background:

  • Hyperspectral images (HSI) contain crucial spectral features sensitive to noise.
  • Noise in specific HSI channels can obscure important spectral information.
  • Maximum Noise Fraction (MNF) is a key technique for HSI noise reduction, requiring accurate noise estimation.

Purpose of the Study:

  • To develop two simple and efficient methods for Noise Covariance Matrix (NCM) estimation for the MNF transform.
  • To improve the performance of HSI classification, especially with mixed ground objects.
  • To enhance the recovery of spectral features in noisy HSI data.

Main Methods:

  • Proposed two novel Noise Covariance Matrix (NCM) estimation techniques.
  • Utilized superpixel-based clustering in the spatial domain for HSI data.
  • Focused on NCM methods with reduced sensitivity to diverse noise distributions and interference patterns.

Main Results:

  • The proposed NCM estimation methods significantly improved HSI classification accuracy.
  • Demonstrated superior performance compared to classical MNF NCM estimation and recent state-of-the-art methods.
  • Showcased enhanced recovery of spectral features in both simulated and real HSI data.

Conclusions:

  • The novel NCM estimation methods offer a significant advancement for MNF-based HSI noise reduction and classification.
  • These techniques are robust against various noise types and improve the reliability of HSI analysis.
  • The improved SNR and spectral feature recovery have broad implications for HSI applications.