Experimental demonstration of an adaptive architecture for direct spectral imaging classification
AffiliationUniv Arizona, Lunar & Planetary Lab
Univ Arizona, Dept Elect & Comp Engn
Univ Arizona, Coll Opt Sci
MetadataShow full item record
PublisherOPTICAL SOC AMER
CitationMatthew Dunlop-Gray, Phillip K. Poon, Dathon Golish, Esteban Vera, and Michael E. Gehm, "Experimental demonstration of an adaptive architecture for direct spectral imaging classification," Opt. Express 24, 18307-18321 (2016)
RightsCopyright © 2016 Optical Society of America. Users may use, reuse, and build upon the article, or use the article for text or data mining, so long as such uses are for non-commercial purposes and appropriate attribution is maintained. All other rights are reserved.
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AbstractSpectral 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. (C) 2016 Optical Society of America
NoteOpen access journal
VersionFinal published version
SponsorsDefense Advanced Research Projects Agency (DARPA) [N66001-10-1-4079]
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