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20 May 2011 Interactive visualization of hyperspectral images on a hyperbolic disk
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Abstract
Visualization of the high-dimensional data set that makes up hyperspectral images necessitates a dimensionality reduction approach to make that data useful to a human analyst. The expression of spectral data as color images, individual pixel spectra plots, principal component images, and 2D/3D scatter plots of a subset of the data are a few examples of common techniques. However, these approaches leave the user with little ability to intuit knowledge of the full N-dimensional spectral data space or to directly or easily interact with that data. In this work, we look at developing an interactive, intuitive visualization and analysis tool based on using a Poincaré disk as a window into that high dimensional space. The Poincaré disk represents an infinite, two-dimensional hyperbolic space such that distances and areas increase exponentially as you move farther from the center of the disk. By projecting N-dimensional data into this space using a non-linear, yet relative distance metric preserving projection (such as the Sammon projection), we can simultaneously view the entire data set while maintaining natural clustering and spacing. The disk also provides a means to interact with the data; the user is presented with a "fish-eye" view of the space which can be navigated and manipulated with a mouse to "zoom" into clusters of data and to select spectral data points. By coupling this interaction with a synchronous view of the data as a spatial RGB image and the ability to examine individual pixel spectra, the user has full control over the data set for classification, analysis, and instructive use.
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Adam A. Goodenough, Ariel Schlamm, Scott D. Brown, and David Messinger "Interactive visualization of hyperspectral images on a hyperbolic disk", Proc. SPIE 8048, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII, 80481K (20 May 2011); https://doi.org/10.1117/12.886927
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