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Dimensionality Reduction on Hyperspectral Data for Solids Analysis

This research explores advanced dimensionality reduction methods applied to hyperspectral data for solids analysis. Utilizing techniques such as Multidimensional Scaling (MDS), Principal Component Analysis (PCA), Locally Linear Embedding (LLE), and Isomap, the study aims to enhance the understanding of information captured across various electromagnetic spectrum wavelengths. By processing hyperspectral images and comparing pixel data of different substances, we strive to deliver valuable preliminary insights for further exploration in solid materials analysis. Ongoing work includes development and execution of additional code for manifold learning techniques.

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Dimensionality Reduction on Hyperspectral Data for Solids Analysis

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  1. Dimensionality Reductionon Hyperspectral Data for Solids Analysis Annalisse Booth Utah State University Electrical and Computer Engineering Department Research Experience for Undergraduates 2009

  2. Hyperspectral Imaging: An Overview • Records information across electromagnetic spectrum • Spectral band correlates to certain range of wavelength • Bands combined to form cube • Hundreds to thousands of bands per cube • 258 bands in current data Source: http://www.yellowstoneresearch.org

  3. Solids Hyperspectral Data • 3 months data • Camera on tripod, but shaken • Cleaned up by Mckay • Turned into video, RGB approximations • Wrote other applicable codes January 11, 2008 17:41:25, wavelength 46

  4. Gathering Tools for Analysis • Multidimensional Scaling (MDS) • Principle Component Analysis (PCA) • Locally Linear Embedding (LLE) • Isomap (weighted geodesic distances) • Maximum Variance Unfolding (MVU) An example of a Locally Linear Embedding (LLE)

  5. Comparing Techniques Source: Boundary Constrained Manifold Unfolding. Bo, Hongbin, Wenan. 2008.

  6. Comparing Techniques Source: Boundary Constrained Manifold Unfolding. Bo, Hongbin, Wenan. 2008.

  7. Comparing Techniques Source: Boundary Constrained Manifold Unfolding. Bo, Hongbin, Wenan. 2008.

  8. Work Still Uncompleted • Write program to choose pixels from each substance through time • Compare pixels of each substance to self and other substances • Analysis in Isomap for preliminary results • Write code for Riemmanian Manifold Learning (RML) • Execute code on data • Write code for Boundary Constrained Manifold Unfolding • Execute new code, compare

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