An adaptive CFAR algorithm for real-time hyperspectral target detection

Abstract

An adaptive algorithm is described for deriving constant false alarm rate (CFAR) detection thresholds based on statistically motivated models of actual spectral detector output distributions. The algorithm dynamically tracks the distribution of detector observables and fits the observed distribution to a suitable mixture density model function. The fitted distribution model is used to compute numerical detection thresholds that achieve a constant probability of false alarm (Pfa) per pixel. Typically gamma mixture densities are used to model outputs of anomaly detectors based on quadratic decision statistics, while normal mixture densities are used for linear matched filter type detectors. In order to achieve the computational efficiency required for real-time implementations of the algorithm on mainstream microprocessors, a robust yet considerably less complex exponential mixture model was recently developed as a general approximation to common long-tailed detector distributions. Within the region of operational interest, namely between the primary mode and the far tail, this approximation serves as an accurate model while providing significant reduction in computational cost. We compare the performance of the exponential approximation against the full-blown gamma and normal models. We also demonstrate the false alarm regulation performance of the adaptive CFAR algorithm using anomaly and matched detector outputs derived from actual VNIR-band hyperspectral imagery collected by the Civil Air Patrol (CAP) Airborne Real time Cueing Hyperspectral Enhanced Reconnaissance (ARCHER) system.