Dual-Mode FPGA Implementation of Target and Anomaly Detection Algorithms for Real-Time Hyperspectral Imaging

Target and anomaly detection are important techniques for remotely sensed hyperspectral data interpretation. Due to the high dimensionality of hyperspectral data and the large computational complexity associated to processing algorithms, developing fast techniques for target and anomaly detection has received considerable attention in recent years. Although several high-performance architectures have been evaluated for this purpose, field programmable gate arrays (FPGAs) offer the possibility of onboard hyperspectral data processing with low-power consumption, reconfigurability and radiation tolerance, which make FPGAs a relevant platform for hyperspectral processing. In this paper, we develop a novel FPGA-based technique for efficient target detection in hyperspectral images. The proposed method uses a streaming background statistics (SBS) approach for optimizing the constrained energy minimization (CEM) and Reed-Xiaoli (RX) algorithms, which are widely used techniques for target and anomaly detection, respectively. Specifically, these two algorithms are implemented in streaming fashion on FPGAs. Most importantly, we present a dual mode that implements a flexible datapath to decide in real time which one among these two algorithms should be used, thus allowing for the dynamic adaptation of the hardware to either target detection or anomaly detection scenarios. Our experiments, conducted with several well-known hyperspectral scenes, indicate the effectiveness of the proposed implementations.