Real-time, standoff detection of trace chemicals on surfaces in the presence of unknown interferent contaminants using active IR spectroscopy poses significant challenges. The measurement time and computational burden can be prohibitive due to the number of spatial pixels, hundreds of potential wavenumbers, and size of the chemical library containing thousands of signatures. Therefore it is advantageous to optimally sample a small subset of the possible wavenumbers, where optimality is meant in the sense of detection and classification performance. Our approach accomplishes this by selecting wavenumbers which maximize the information about chemical identity. This is done by using submodular optimization, a technique which guarantees near-optimality at vanishingly low computational burden. Therefore, the methods shown here lend themselves to the time and resource constrained problems of data acquisition. This is in contrast to more traditional dimensionality reduction approaches such as lowering spectral resolution, random sparse sampling, and principal component analysis which degrade detection performance. In this work we describe methods for optimal illumination wavenumber selection to address the time constraints while addressing the challenges imposed by hardware and environmental artifacts (e.g., atmospheric effects).
Gil Raz, Mark Chilenski, Cara Murphy, and Chelsea Georgan, "Optimal sensor control for fast target detection in hyperspectral imagery," Proc. SPIE 10644, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV, 1064407 (Presented at SPIE Defense + Security: April 17, 2018; Published: 8 May 2018); https://doi.org/10.1117/12.2304050.
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