Maintaining CFAR Operation in Hyperspectral Target Detection Using Extreme Value Distributions

摘要

One of the primary motivations for statistical LWIR background characterization studies is to support the design, evaluation, and implementation of algorithms for the detection of various types of ground targets. Typically, detection is accomplished by comparing the detection statistic for each test pixel to a threshold. If the statistic exceeds the threshold, a potential target is declared. The threshold is usually selected to achieve a given probability of false alarm. In addition, in surveillance applications, it is almost always required that the system will maintain a constant false alarm rate (CFAR) as the background distribution changes. This objective is usually accomplished by adaptively estimating the background statistics and adjusting the threshold accordingly. In this paper we propose and study CFAR threshold selection techniques, based on tail extrapolation, for a detector operating on hyperspectral imaging data. The basic idea is to obtain reliable estimates of the background statistics at low false alarm rates, and then extend these estimates beyond the range supported by the data to predict the thresholds at lower false alarm rates. The proposed techniques are based on the assumption that the distribution in the tail region of the detection statistics is accurately characterized by a member of the extreme value distributions. We focus on the generalized Pareto distribution. The evaluation of the proposed techniques will be done with both simulated data and real hyperspectral imaging data collected using the Army Night Vision Laboratory COMPASS sensor.