AN FPGA-Based Implementation of A Hyperspectral Anomaly Detection Algorithm for Real-Time Applications

Abstract

Remote sensing has gained relevance in the last years, mainly due to the emergence of UAVs carrying airborne imagery sensors. In this regard, the on-board data processing for on-the-fly making-decision applications is also gaining momentum. Nevertheless, these flight vehicles are still limited in terms of power budget and computational capacity, which hampers the handling of the hyperspectral data. Consequently, there is an emerging trend towards the development of more hardware-friendly algorithms suitable for an efficient implementation in parallel computing devices. In this sense, the LbL-FAD algorithm arose in response to the lack of causal anomaly detectors that could be easily integrated in push-broom-based acquisition systems. In this work, we have analysed the feasibility of the performance and power needs of the LbL-FAD algorithm in a mid-range re-configurable FPGA-SoC such as the XC7Z020 chip. Concretely, a highly optimized FPGA accelerator of the LbL-FAD method has been described for the line-by-line detection of anomalous spectra.