Real-time analysis of hyperspectral data in MATLAB: theoretical limits of anomaly detection utilizing higher order statistics through simulation

Eric R. Languirand; Darren K. Emge

doi:10.1117/12.2585927

12 April 2021 Real-time analysis of hyperspectral data in MATLAB: theoretical limits of anomaly detection utilizing higher order statistics through simulation

Eric R. Languirand, Darren K. Emge

Author Affiliations +

Proceedings Volume 11749, Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XXII; 117490Q (2021) https://doi.org/10.1117/12.2585927
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

The inherent wealth of information associated with hyperspectral data provides a data stream that could be leveraged for situational awareness or providing immediate user feedback. However, the enormous amount of data that is produced by some system’s data stream requires longer processing times and often post-processing techniques. Therefore, it is prudent to develop real-time hyperspectral processing techniques that are capable of operating at maneuver speeds. Anomaly detection techniques applied to higher order statistics of the hyperspectral data can provide immediate user feedback for awareness. Determining capabilities prior to applying directly to a system is also informative and provides an in silico point of reference. In this paper, we show, through the use of a real-time simulator (RTS) in the MATLAB environment, a method for simulating the processing speed of a data stream based on how data is received from the instrument. In this work, the RTS provides sub 100ms capabilities based on non-optimized code within the MATLAB environment and is largely limited by the write speed in MATLAB. Utilizing virtual memory and the flexibility of MATLAB allows for simulating real-time capabilities of already obtained hyperspectral data prior to implementing it on a device. Additionally, applying the algorithm to a simulated ground truth data provides a theoretical limit of anomaly detection (LOAD). We further compare theoretical LOADs with actual anomaly detection capabilities in a laboratory environment.

Conference Presentation

Citation Download Citation

Eric R. Languirand and Darren K. Emge "Real-time analysis of hyperspectral data in MATLAB: theoretical limits of anomaly detection utilizing higher order statistics through simulation", Proc. SPIE 11749, Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XXII, 117490Q (12 April 2021); https://doi.org/10.1117/12.2585927

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