A non-parametric approach to anomaly detection in hyperspectral images

Tiziana Veracini; Stefania Matteoli; Marco Diani; Giovanni Corsini; Sergio U. de Ceglie

doi:10.1117/12.865073

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22 October 2010 A non-parametric approach to anomaly detection in hyperspectral images

Tiziana Veracini, Stefania Matteoli, Marco Diani, Giovanni Corsini, Sergio U. de Ceglie

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Proceedings Volume 7830, Image and Signal Processing for Remote Sensing XVI; 78300B (2010) https://doi.org/10.1117/12.865073
Event: SPIE Remote Sensing, 2010, Toulouse, France

Abstract

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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Tiziana Veracini, Stefania Matteoli, Marco Diani, Giovanni Corsini, and Sergio U. de Ceglie "A non-parametric approach to anomaly detection in hyperspectral images", Proc. SPIE 7830, Image and Signal Processing for Remote Sensing XVI, 78300B (22 October 2010); https://doi.org/10.1117/12.865073

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