Snapshot hyperspectral imaging Fourier transform (SHIFT) spectrometers are a promising technology in optical detection and target identification. For any imaging spectrometer, spatial, spectral, and temporal resolution, along with form factor, power consumption, and computational complexity are often the design considerations for a desired application. Motivated by the need for high spectral resolution systems, capable of real-time implementation, we demonstrate improvements to the spectral resolution and computation trade-space. In this paper, we discuss the implementation of spatial heterodyning, using polarization gratings, to improve the spectral resolution trade space of a SHIFT spectrometer. Additionally, we employ neural networks to reduce the computational complexity required for data reduction, as appropriate for real-time imaging applications. Ultimately, with this method we demonstrate an 87% decrease in processing steps when compared to Fourier techniques. Additionally, we show an 80% reduction in spectral reconstruction error and a 30% increase in spatial fidelity when compared to linear operator techniques.

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