Generalized linear feature detection of weak targets in spectrally mixed clutter

Xiaoli Yu; Lawrence E. Hoff; Scott G. Beaven; Edwin M. Winter; John A. Antoniades; Irving S. Reed

doi:10.1117/12.283957

29 October 1997 Generalized linear feature detection of weak targets in spectrally mixed clutter

Xiaoli Yu, Lawrence E. Hoff, Scott G. Beaven, Edwin M. Winter, John A. Antoniades, Irving S. Reed

Proceedings Volume 3163, Signal and Data Processing of Small Targets 1997; (1997) https://doi.org/10.1117/12.283957
Event: Optical Science, Engineering and Instrumentation '97, 1997, San Diego, CA, United States

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.

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Xiaoli Yu, Lawrence E. Hoff, Scott G. Beaven, Edwin M. Winter, John A. Antoniades, and Irving S. Reed "Generalized linear feature detection of weak targets in spectrally mixed clutter", Proc. SPIE 3163, Signal and Data Processing of Small Targets 1997, (29 October 1997); https://doi.org/10.1117/12.283957

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KEYWORDS

Target detection

Sensors

Algorithm development

Buildings

Detection and tracking algorithms

Signal to noise ratio

Data fusion

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