Multimode spectral data fusion using supervised and reinforcement learning for mislabeling (Conference Presentation)

Our goal is to use multimode spectral imaging data from four optical imaging systems and derive statistics to help us identify fish species from the multimode fusion of spectral data. The spectral data have been acquired using four systems: (1) Reflectance spectral imaging in the visible and near-infrared, (2) reflectance spectral imaging in the short-wave infrared, (3) fluorescence spectral imaging, (4) Raman imaging. We classify data and build decision tables. We then weight our tables with Monte Carlo prediction to increase the sensitivity of the system.