Abstract

We have developed an adaptive spectral imaging classifier that directly spectrally-classifies every location in a scene and adaptively adjusts itself to facilitate this classification process. The net result is classification error rates that are multiple orders-of-magnitude below those of conventional instruments or even static computational/compressive sensing approaches. I will present our latest results and demonstrate quantitative agreement with expected performance across a wide range of operational parameters.

© 2015 Optical Society of America

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Presentation Video

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More Like This
Information-Optimal Adaptive Spectral Classification Imaging

P.A. Jansen, Y.I. Rodriguez, and M.E. Gehm
FThY2 Frontiers in Optics (FiO) 2011

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