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Analysis of electron energy loss spectroscopy data using geometric extraction methods.

Jakob Spiegelberg1, Ján Rusz1, Thomas Thersleff2

  • 1Department of Physics and Astronomy, Uppsala University, Box 516, S-751 20 Uppsala, Sweden.

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Summary

This study introduces advanced geometric data decomposition methods, including randomized vertex component analysis (RVCA) for noisy data and minimum volume simplex analysis (MVSA) for endmember extraction without pure pixels. These techniques improve spectral signature analysis, especially for high-dimensional datasets.

Keywords:
Blind source separationData clusteringEELSGeometric extraction methodsRVCA

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Area of Science:

  • Geospatial analysis
  • Signal processing
  • Data science

Background:

  • Geometric data decomposition is crucial for analyzing complex datasets.
  • Existing methods like vertex component analysis (VCA) have limitations with noisy or impure data.

Purpose of the Study:

  • To establish and evaluate advanced geometric data decomposition methods.
  • To address challenges in endmember extraction from hyperspectral data, particularly in the absence of pure pixels.
  • To assess the performance of these methods on high-dimensional data.

Main Methods:

  • Randomized Vertex Component Analysis (RVCA) for noisy data.
  • Minimum Volume Simplex Analysis (MVSA) for endmember extraction without pure pixels.
  • Comparison of MVSA with Bayesian Linear Unmixing (BLU).
  • Evaluation of Gaussian Mixture Modeling for spectral signature extraction.

Main Results:

  • RVCA extends VCA for improved performance on noisy datasets.
  • MVSA offers a viable approach for endmember extraction when pure pixels are unavailable.
  • Gaussian Mixture Modeling enhances the spectral signatures of extracted source components.

Conclusions:

  • The presented geometric data decomposition methods, RVCA and MVSA, offer significant improvements for analyzing complex and high-dimensional data.
  • Gaussian Mixture Modeling further refines spectral signature extraction, enhancing the utility of these decomposition techniques.