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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.