Dimensionality Reduction on Hyperspectral Data for Solids Analysis
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Transcript of Dimensionality Reduction on Hyperspectral Data for Solids Analysis
Dimensionality Reduction on Hyperspectral Data for
Solids Analysis
Annalisse BoothUtah State University
Electrical and Computer Engineering DepartmentResearch Experience for Undergraduates 2009
Hyperspectral Imaging: An Overview
Source: http://www.yellowstoneresearch.org
• 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
January 11, 2008 17:41:25, wavelength 46
Solids Hyperspectral Data
• 3 months data
• Camera on tripod, but shaken
• Cleaned up by Mckay
• Turned into video, RGB approximations
• Wrote other applicable codes
Gathering Tools for Analysis
An example of a Locally Linear Embedding (LLE)
• Multidimensional Scaling (MDS)• Principle Component Analysis (PCA)• Locally Linear Embedding (LLE)• Isomap (weighted geodesic distances)• Maximum Variance Unfolding (MVU)
Comparing Techniques
Source: Boundary Constrained Manifold Unfolding. Bo, Hongbin, Wenan. 2008.
Comparing Techniques
Source: Boundary Constrained Manifold Unfolding. Bo, Hongbin, Wenan. 2008.
Comparing Techniques
Source: Boundary Constrained Manifold Unfolding. Bo, Hongbin, Wenan. 2008.
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