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Proceedings Paper

Generalized linear feature detection of weak targets in spectrally mixed clutter
Author(s): Xiaoli Yu; Lawrence E. Hoff; Scott G. Beaven; Edwin M. Winter; John A. Antoniades; Irving S. Reed
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Paper Abstract

The ability to detect weak targets of low contrast or signal-to- noise ratio (SNR) is improved by a fusion of data in space and wavelength from multispectral/hyperspectral sensors. It has been demonstrated previously that the correlation of the clutter between multiband thermal infrared images plays an important role in allowing the data collected in one spectral band to be used to cancel the background clutter in another spectral band, resulting in increased SNR. However, the correlation between bands is reduced when the spectrum observed in each pixel is derived from a mixture of several different materials, each with its own spectral characteristics. In order to handle the identification of objects in this complex (mixed) clutter, a class of algorithms have been developed that model the pixels as a linear combination of pure substances and then unmix the spectra to identify the pixel constituents. In this paper a linear unmixing algorithm is incorporated with a statistical hypothesis test for detecting a known target spectral feature that obeys a linear mixing model in a mixture of background noise. The generalized linear feature detector utilizes a maximum likelihood ratio approach to detect and estimate the presence and concentration of one or more specific objects. A performance evaluation of the linear unmixing and maximum likelihood detector is shown by comparing the results to the spectral anomaly detection algorithm previously developed by Reed and Yu.

Paper Details

Date Published: 29 October 1997
PDF: 9 pages
Proc. SPIE 3163, Signal and Data Processing of Small Targets 1997, (29 October 1997); doi: 10.1117/12.283957
Show Author Affiliations
Xiaoli Yu, Science Applications International Corp. (United States)
Lawrence E. Hoff, Naval Command, Control and Ocean Surveillance Ctr. (United States)
Scott G. Beaven, Naval Command, Control and Ocean Surveillance Ctr. (United States)
Edwin M. Winter, Technical Research Associates, Inc. (United States)
John A. Antoniades, Naval Research Lab. (United States)
Irving S. Reed, Univ. of Southern California (United States)

Published in SPIE Proceedings Vol. 3163:
Signal and Data Processing of Small Targets 1997
Oliver E. Drummond, Editor(s)

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