Experimental demonstration of an adaptive architecture for direct spectral imaging classification.

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Dunlop-Gray, M; Poon, PK; Golish, D; Vera, E; Gehm, ME

Published Date

  • August 2016

Published In

Volume / Issue

  • 24 / 16

Start / End Page

  • 18307 - 18321

PubMed ID

  • 27505794

Pubmed Central ID

  • 27505794

Electronic International Standard Serial Number (EISSN)

  • 1094-4087

International Standard Serial Number (ISSN)

  • 1094-4087

Digital Object Identifier (DOI)

  • 10.1364/oe.24.018307

Language

  • eng