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Proceedings Paper

Random subspaces and SAR classification efficacy
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Paper Abstract

The 'curse of dimensionality' has limited the application of statistical modeling techniques to low-dimensional spaces, but typical data usually resides in high-dimensional spaces (at least initially, for instance images represented as arrays of pixel values). Indeed, approaches such as Principal Component Analysis and Independent Component Analysis attempt to extract a set of meaningful linear projections while minimizing interpoint distance distortions. The counterintuitive yet effective random projections approach of Johnson and Lindenstrauss defines a sample-based dimensionality reduction technique with probabilistically provable distortion bounds. We investigate and report on the relative efficacy of two random projection techniques for Synthetic Aperture Radar images in a classification setting.

Paper Details

Date Published: 19 May 2005
PDF: 12 pages
Proc. SPIE 5808, Algorithms for Synthetic Aperture Radar Imagery XII, (19 May 2005); doi: 10.1117/12.602523
Show Author Affiliations
Donald Waagen, Raytheon Co./Missile Systems (United States)
Nitesh Shah, Raytheon Co./Missile Systems (United States)
Miguel Ordaz, Raytheon Co./Missile Systems (United States)
Mary Cassabaum, Raytheon Co./Missile Systems (United States)


Published in SPIE Proceedings Vol. 5808:
Algorithms for Synthetic Aperture Radar Imagery XII
Edmund G. Zelnio; Frederick D. Garber, Editor(s)

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