Performance evaluation of hyperspectral chemical detection systems

• Online Access: http://hdl.handle.net/2047/D20195500

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 plume's chemical constituents, based on a chemical library, is a multiple hypothesis-testing problem that standard detection metrics do not fully describe. Our approach partitions and weights a confusion matrix to develop both the standard detection metrics and an identification metric based on the Dice index. Using the developed metrics, we demonstrate that using a detector bank followed by an identifier can achieve superior performance relative to either algorithm individually.