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, Steven E. Golowich, Dimitris G. Manolakis, 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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