
Experimental demonstration of an adaptive architecture for direct spectral imaging classification.
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.
Duke Scholars
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Optics
- 5102 Atomic, molecular and optical physics
- 4009 Electronics, sensors and digital hardware
- 4006 Communications engineering
- 1005 Communications Technologies
- 0906 Electrical and Electronic Engineering
- 0205 Optical Physics
Citation

Published In
DOI
EISSN
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Optics
- 5102 Atomic, molecular and optical physics
- 4009 Electronics, sensors and digital hardware
- 4006 Communications engineering
- 1005 Communications Technologies
- 0906 Electrical and Electronic Engineering
- 0205 Optical Physics