Performance Evaluation of Hyperspectral Chemical Identification Systems
Remote sensing of chemical vapour plumes is a difficult but important task with many military and civilian applications. Hyperspectral sensors operating in the long wave infrared have well demonstrated plume detection capabilities. However, identification of a plume's chemical constituents, using a chemical signature library, is a multiple hypothesis testing problem that standard detection performance metrics do not fully characterize. We propose using an additional performance metric for chemical identification based of the so-called Dice index. Using detection metrics and the proposed performance metric, we demonstrate that the intuitive system design of a detector bank followed by an identifier is justified when the additional metric is considered.