Experimental demonstration of an adaptive architecture for direct spectral imaging classification

Matthew Dunlop-Gray, Phillip K. Poon, Dathon Golish, Esteban Vera, Michael E. Gehm

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


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.

Original languageEnglish
Pages (from-to)18307-18321
Number of pages15
JournalOptics Express
Issue number16
StatePublished - 8 Aug 2016


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