We present an architecture for rapid spectral classification in spectral imaging applications. By making use of knowledge
gained in prior measurements, our spectral imaging system is able to design adaptive feature-specific measurement
kernels that selectively attend to the portions of a spectrum that contain useful classification information. With
measurement kernels designed using a probabilistically-weighted version of principal component analysis, simulations
predict an orders-of-magnitude reduction in classification error rates. We report on our latest simulation results, as well
as an experimental prototype currently under construction.