Partially supervised detection using band subset selection in hyperspectral data

Recent development of more sophisticated sensors enable the measurement of radiation in many more spectral intervals at a higher spectral resolution than previously possible. As the number of bands in high spectral resolution data increases, the capability to detect more objects and the detection accuracy should increase as well. Most of the detection techniques presently used in hyperspectral data require the use of spectral libraries that contain information on specific objects to be detected. An example of one technique used for detection purposes in hyperspectral imagery is the spectral angle approach based on the Euclidean inner product of the spectral signatures. This method has good performance on objects that have sufficient differences between their spectral signatures. This paper presents a partially supervised detection approach that uses previously measured spectral responses as inputs and is capable of differentiating objects that have similar spectral signatures. Two versions will be presented: one that is based on Statistical Pattern Recognition and other based on Fuzzy Pattern Recognition. The detection mechanisms are tested with objects of very similar spectral signatures and the detection results are compared with those from the spectral angle approach.