Reinforcement-learning-guided feature wavenumber selection in hyperspectral imaging

Hyperspectral imaging provides rich spectral and spatial data but often collects unnecessary information, leading to inefficiencies. To address this, we developed the Explorative Spectral Acquisition Guide (ESAG), which optimizes the selection of essential wavenumbers, with or without priors, avoiding redundant spectral bands irrelevant to downstream tasks. In hyperspectral stimulated Raman scattering imaging, ESAG significantly reduced the number of spectral frames needed. Notably, ESAG achieved a lower error rate on unmixing tasks using only 60% of the spectral frames compared to the full spectrum. In segmentation tasks involving polymer mixtures, ESAG attained 93.96% average accuracy with only 50% of the spectral window. Interestingly, for pure polymer segmentation, the accuracy of ESAG using 70% of the spectral window surpassed that of the full spectrum. Overall, ESAG enhances both the efficiency and accuracy of hyperspectral imaging, with broad potential applications in chemistry and biomedicine.