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Proceedings Paper

Clutter and anomaly removal for enhanced target detection
Author(s): William F. Basener
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Paper Abstract

In this paper we investigate the use of anomaly detection to identify pixels to be removed prior to covariance computation. The resulting covariance matrix provides a better model of the image background and is less likely to be tainted by target spectra. In our tests, this method results in robust improvement in target detection performance for quadratic detection algorithms. Tests are conducted using imagery and targets freely available online. The imagery was acquired over Cooke City, Montana, a small town near Yellowstone Park, using the HyMap V/NIR/SWIR sensor with 126 spectral bands. There are three vehicle and four fabric targets located in the town and surrounding area.

Paper Details

Date Published: 13 May 2010
PDF: 15 pages
Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 769525 (13 May 2010); doi: 10.1117/12.850303
Show Author Affiliations
William F. Basener, Rochester Institute of Technology (United States)


Published in SPIE Proceedings Vol. 7695:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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