Performance Metrics for the Evaluation of Hyperspectral Chemical Identification Systems
MIT Lincoln Laboratory Lexington United States
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Remote sensing of chemical vapor plumes is a difficult but important task with many military and civilian applications.Hyperspectral sensors operating in the long wave infrared LWIR regime have well demonstrated detection capabilities. However, the identification of a plumes 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.
- Infrared Detection and Detectors
- Chemical, Biological and Radiological Warfare