Hyperspectral imagery: clutter adaptation in anomaly detection

  • Schweizer S
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Hyperspectral sensors are passive sensors that simultaneously
record images for hundreds of contiguous and narrowly spaced regions of
the electromagnetic spectrum. Each image corresponds to the same ground
scene, thus creating a cube of images that contain both spatial and
spectral information about the objects and backgrounds in the scene. In
this paper, we present an adaptive anomaly detector designed assuming
that the background clutter in the hyperspectral imagery is a
three-dimensional Gauss-Markov random field. This model leads to an
efficient and effective algorithm for discriminating man-made objects
(the anomalies) in real hyperspectral imagery. The major focus of the
paper is on the adaptive stage of the detector, i.e., the estimation of
the Gauss-Markov random field parameters. We develop three methods:
maximum-likelihood; least squares; and approximate maximum-likelihood.
We study these approaches along three directions: estimation error
performance, computational cost, and detection performance. In terms of
estimation error, we derive the Cramer-Rao bounds and carry out Monte
Carlo simulation studies that show that the three estimation procedures
have similar performance when the fields are highly correlated, as is
often the case with real hyperspectral imagery. The approximate
maximum-likelihood method has a clear advantage from the computational
point of view. Finally, we test extensively with real hyperspectral
imagery the adaptive anomaly detector incorporating either the least
squares or the approximate maximum-likelihood estimators. Its
performance compares very favorably with that of the RX algorithm

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  • Susan M. Schweizer

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