Higher order statistics for anomaly detection in hyper spectral imaging

Eric R. Languirand; Darren K. Emge

doi:10.1117/12.2518726

22 July 2019 Higher order statistics for anomaly detection in hyper spectral imaging

Eric R. Languirand, Darren K. Emge

Proceedings Volume 11010, Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XX; 110100J (2019) https://doi.org/10.1117/12.2518726
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

Hyperspectral imaging (HSI) has become increasingly popular for sensing in defense, commercial, and academic research for its ability to acquire vast amounts of information, relatively quickly, at stand-off distances. As such, the need for rapid or near-real time data reduction is becoming more evident especially when immediate knowledge of the area under investigation is required such as in contested areas, the scene of natural disasters, and other similar scenarios. While analysis of the underlying spectral information may provide specific information about materials present, in HSI determining an anomaly can be just as informative in scenarios such as CB detection for avoidance. Therefore, a rapid, real-time HSI anomaly detection algorithm is merited. In this paper, we present work towards an algorithm for near-real time anomaly detection utilizing higher-order statistics and, in particular, implications due to changes in skewness and kurtosis, the 3rd and 4th central moments. We demonstrate using a visible-SWIR hyperspectral line scanner that anomalies (thiodiglycol and acetaminophen) can be detected in data that is updated to simulate real-time analysis. Changing spectral features result in changes in the probability density function, and can be specifically realized with comparisons of higher order statistics (i.e. skewness and kurtosis), thereby reducing a full spectral analysis at each voxel to a comparison of two values at each pixel. This paper explores utilizing this concept as a means for anomaly detection, evaluating different surfaces that an analyte may be present on, and lastly presents work towards automated background updates for anomaly detection on dynamic surfaces.

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Eric R. Languirand and Darren K. Emge "Higher order statistics for anomaly detection in hyper spectral imaging", Proc. SPIE 11010, Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XX, 110100J (22 July 2019); https://doi.org/10.1117/12.2518726

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KEYWORDS

Statistical analysis

Scanners

Data processing

Signal to noise ratio

Imaging spectroscopy

Hyperspectral imaging

Stochastic processes

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