FPGA Implementation for Hyperspectral Target Detection with Adaptive Coherence Estimator

Field-programmable gate arrays (FPGAs) offer promising tools for real-time on-board hyperspectral image (HSI) processing due to its higher computational speed and better reconfigurability. In this paper, the spectral matching target detection algorithm, namely adaptive coherence estimator (ACE), is implemented based on FPGA. The flow background statistics are used to calculate the background covariance matrix, and the Sherman-Morrison method is further used to process the covariance matrix inversion. The finally calculated results should separate targets from the whole pixels by a selected threshold. Evaluation of several hyperspectral data shows that the system has a higher speed than the traditional 3.60GHz CPU (about 77.74 times) without losing accuracy.