In the past few years, spectral analysis of data collected by hyperspectral sensors aimed at automatic anomaly detection
has become an interesting area of research. In this paper, we are interested in an Anomaly Detection (AD) scheme for
hyperspectral images in which spectral anomalies are defined with respect to a statistical model of the background Probability
Density Function (PDF).The characterization of the PDF of hyperspectral imagery is not trivial. We approach the
background PDF estimation through the Parzen Windowing PDF estimator (PW). PW is a flexible and valuable tool for
accurately modeling unknown PDFs in a non-parametric fashion. Although such an approach is well known and has been
widely employed, its use within an AD scheme has been not investigated yet. For practical purposes, the PW ability to
estimate PDFs is strongly influenced by the choice of the bandwidth matrix, which controls the degree of smoothing of
the resulting PDF approximation. Here, a Bayesian approach is employed to carry out the bandwidth selection. The resulting
estimated background PDF is then used to detect spectral anomalies within a detection scheme based on the
Neyman-Pearson approach. Real hyperspectral imagery is used for an experimental evaluation of the proposed strategy.
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