Journal
ADVANCED OPTICAL TECHNOLOGIES
Volume 6, Issue 2, Pages 85-93Publisher
WALTER DE GRUYTER GMBH
DOI: 10.1515/aot-2016-0068
Keywords
active stand-off spectroscopy; hyperspectral data analysis; QCL
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Funding
- European Communitys Seventh Framework Program (FP7) [317884 MIRIFISENS]
- Horizon program [645535 CHEQUERS, 688265 MIRPHAB]
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In this work we present data analysis algorithms for detection of hazardous substances in hyperspectral observations acquired using active mid-infrared (MIR) backscattering spectroscopy. We present a novel background extraction algorithm based on the adaptive target generation process proposed by Ren and Chang called the adaptive background generation process (ABGP) that generates a robust and physically meaningful set of background spectra for operation of the well-known adaptive matched subspace detection (AMSD) algorithm. It is shown that the resulting AMSD-ABGP detection algorithm competes well with other widely used detection algorithms. The method is demonstrated in measurement data obtained by two fundamentally different active MIR hyperspectral data acquisition devices. A hyperspectral image sensor applicable in static scenes takes a wavelength sequential approach to hyperspectral data acquisition, whereas a rapid wavelength-scanning single-element detector variant of the same principle uses spatial scanning to generate the hyperspectral observation. It is shown that the measurement timescale of the latter is sufficient for the application of the data analysis algorithms even in dynamic scenarios.
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