10 February 2016 Performance metrics for the evaluation of hyperspectral chemical identification systems
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Abstract
Remote sensing of chemical vapor plumes is a difficult but important task for many military and civilian applications. Hyperspectral sensors operating in the long-wave infrared regime have well-demonstrated detection capabilities. However, the identification of a plume’s chemical constituents, based on a chemical library, is a multiple hypothesis testing problem which standard detection metrics do not fully describe. We propose using an additional performance metric for identification based on the so-called Dice index. Our approach partitions and weights a confusion matrix to develop both the standard detection metrics and identification metric. Using the proposed metrics, we demonstrate that the intuitive system design of a detector bank followed by an identifier is indeed justified when incorporating performance information beyond the standard detection metrics.
© 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)
Eric Truslow, Eric Truslow, Steven E. Golowich, Steven E. Golowich, Dimitris G. Manolakis, Dimitris G. Manolakis, Vinay K. Ingle, Vinay K. Ingle, } "Performance metrics for the evaluation of hyperspectral chemical identification systems," Optical Engineering 55(2), 023106 (10 February 2016). https://doi.org/10.1117/1.OE.55.2.023106 . Submission:
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