A Low-Complexity Hyperspectral Anomaly Detection Algorithm and Its FPGA Implementation

On-board real-time anomaly detection has always been a challenging task in hyperspectral imaging analysis as it requires low computational complexity. Most of the existing anomaly detection algorithms inevitably trade off intensive computational complexity for high detection accuracy. This article presents a fast spectral-spatial anomaly detection algorithm with low complexity in hyperspectral images (HSIs) using morphological reconstruction and a simplified guided filter (Fast-MGD). Since the simple filtering techniques are applied, it is therefore feasible to achieve a field programmable gate array (FPGA)-based hardware implementation. More precisely, an effective deeply pipelined acceleration scheme is developed adopting high-level synthesis to support HSIs that are acquired over different scenes with different sizes and spectral bands. Experimental results show strong advantages of the proposed FPGA-based Fast-MGD in processing speed and resource consumption, while a high detection accuracy is remained. Its applicability in on-board real-time processing is demonstrated and verifie.