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Hyperspectral imaging is gathering significant attention due to its potentialin various domains such as geology, agriculture, ecology, and surveillance.However, the associated processing algorithms, which are essential forenhancing output quality and extracting relevant information, are oftencomputationally intensive and have to deal with substantial data volumes. Ourfocus lies on reconfigurable hardware, particularly recent FPGAs. While FPGAdesign can be complex, High Level Synthesis (HLS) workflows have emerged as asolution, abstracting low-level design intricacies and enhancing productivity.Despite successful prior efforts using HLS for hyperspectral imagingacceleration, we lack a comprehensive research to benchmark various algorithmsand architectures within a unified framework. This study aims to quantitativelyevaluate performance across different inversion algorithms and designarchitectures, providing insights for optimal trade-offs for specificapplications. We apply this analysis to the case study of spectrumreconstruction processed from interferometric acquisitions taken by Fouriertransform spectrometers.

Implementation of hyperspectral inversion algorithms on FPGA: Hardware comparison using High Level Synthesis