TY - JOUR
T1 - Experimental demonstration of an adaptive architecture for direct spectral imaging classification
AU - Dunlop-Gray, Matthew
AU - Poon, Phillip K.
AU - Golish, Dathon
AU - Vera, Esteban
AU - Gehm, Michael E.
N1 - Funding Information:
Defense Advanced Research Projects Agency (DARPA) (N66001-10-1-4079).
Publisher Copyright:
© 2016 Optical Society of America.
PY - 2016/8/8
Y1 - 2016/8/8
N2 - Spectral imaging is a powerful tool for providing in situ material classification across a spatial scene. Typically, spectral imaging analyses are interested in classification, though often the classification is performed only after reconstruction of the spectral datacube. We present a computational spectral imaging system, the Adaptive Feature-Specific Spectral Imaging Classifier (AFSSI-C), which yields direct classification across the spatial scene without reconstruction of the source datacube. With a dual disperser architecture and a programmable spatial light modulator, the AFSSI-C measures specific projections of the spectral datacube which are generated by an adaptive Bayesian classification and feature design framework. We experimentally demonstrate multiple order-of-magnitude improvement of classification accuracy in low signal-to-noise (SNR) environments when compared to legacy spectral imaging systems.
AB - Spectral imaging is a powerful tool for providing in situ material classification across a spatial scene. Typically, spectral imaging analyses are interested in classification, though often the classification is performed only after reconstruction of the spectral datacube. We present a computational spectral imaging system, the Adaptive Feature-Specific Spectral Imaging Classifier (AFSSI-C), which yields direct classification across the spatial scene without reconstruction of the source datacube. With a dual disperser architecture and a programmable spatial light modulator, the AFSSI-C measures specific projections of the spectral datacube which are generated by an adaptive Bayesian classification and feature design framework. We experimentally demonstrate multiple order-of-magnitude improvement of classification accuracy in low signal-to-noise (SNR) environments when compared to legacy spectral imaging systems.
UR - http://www.scopus.com/inward/record.url?scp=84987657241&partnerID=8YFLogxK
U2 - 10.1364/OE.24.018307
DO - 10.1364/OE.24.018307
M3 - Article
AN - SCOPUS:84987657241
VL - 24
SP - 18307
EP - 18321
JO - Optics Express
JF - Optics Express
SN - 1094-4087
IS - 16
ER -