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Visualizing Spectral Decomposition Using the View Locked Color Image Grand Tour

Visualizing Spectral Decomposition Using the View Locked Color Image Grand Tour. Bradley Wallet University of Oklahoma bwallet@ou.edu. Where I’m From. Outline. Dimensionality Reduction Principal Component Analysis Image Grand Tour Synthetic Results View Locked Color IGT Results

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Visualizing Spectral Decomposition Using the View Locked Color Image Grand Tour

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  1. Visualizing Spectral Decomposition Using the View Locked Color Image Grand Tour Bradley Wallet University of Oklahoma bwallet@ou.edu

  2. Where I’m From

  3. Outline • Dimensionality Reduction • Principal Component Analysis • Image Grand Tour • Synthetic Results • View Locked Color IGT • Results • List of Approved Questions

  4. Dimensionality Reduction • Many attributes • Spectral components in remote sensing • Curvature, coherence, RMS amplitude, phase in seismic interpretation • Gravity, magnetics, hyperspectral imagery, geological maps • Each attribute is a dimension of a space • Interaction can provide additional information • Difficult to visualize more than three dimensions • Mathematical considerations

  5. Linear Projections to Reduce Dimensionality and Separate Clusters

  6. Linear Projections Red Component Green Component Blue Component

  7. How do you human beings interpret images?

  8. Principal Component Analysis: • Converts multiple non-orthogonal attribute data into a (smaller subset of) orthogonal principal components • The first PCA represents the most variability of the data • Efficiently represents the data with a minimal number of components, however, these components may not correspond to human perception • Represents the most energetic data components • May not represent low-energy components of interpretation interest

  9. Input attributes

  10. Desired images hidden in attribute space

  11. 6 Principal Components(spatial and heuristic information are not exploited)

  12. The Grand Tour (1750s-1880s)

  13. Grand Tour • Grand Tour attempts an exhaustive search of projections of the data (given infinite time!) • Projections based upon a continuous curve in space of possible projections • Smooth transitions are presented as a movie of data projections • Results are typically displayed as a scatter/cross plot • Ignores the inherent spatial nature of the data

  14. Defining the grand tour of everything… A sugar-coated Grassmannian manifold…

  15. Image Grand Tour Workflow • Display projections as images using original topology (e.g. maps) • Identify meaningful projections using “domain” (geological and geophysical) expertise • Capture or save interpretationally ‘independent’ images for later analysis

  16. Synthetic Example

  17. View Locked Color IGT Workflow • Find first image using IGT • Lock first image in the red component and continue tour in blue/green (cyan) component • Lock second image in the green component and continue tour in the blue component

  18. Application

  19. Application

  20. Conclusions • Spatial organization matters! • IGT exploits the pattern-recognition capabilities of human interpreters • View Locked Color IGT allows for the interpretation of multiple and/or complex processes • More work needs to be done in workflow design

  21. Acknowledgements • Attribute Assisted Seismic Processing and Interpretation Consortium (http://geology.ou.edu/aaspi) • Anadarko Petroleum • Dr. Juergen Symanzik of Utah State University • Dr. Kurt Marfurt of the University of Oklahoma

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