Recent advances in photonics and imaging technology allow the development of cutting-edge, lightweight hyperspectral sensors, both push-broom/line-scanning and snapshot/frame. At the same time, emerging applications in robotics, food inspection, medicine and earth observation are posing critical challenges on real-time processing and computational efficiency, both in terms of accuracy and power consumption. In this direction, in the current paper, we accelerate hyperspectral processing kernels by utilizing FPGAs, i.e., Zynq-7000 SoC, to perform similaritybased matching of spectral signatures. We propose a custom HW architecture based on multi-level parallelization, modularity, and parametric VHDL coding, which allows for in-depth design space exploration and trade-off analysis. Depending on configuration, our implementation processes 22−107 Megapixels per second providing an acceleration of 40−355x vs Intel-i3 CPU and 360−10 4 x vs the embedded ARM Cortex A9, whereas the overall detection quality ranges from 56% to 97% when evaluated with multiple objects and images of 285 spectral channels.

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