A real-time CFAR thresholding method for target detection in hyperspectral images

In order to support immediate decision-making in critical circumstances such as military reconnaissance and disaster rescue, real-time onboard implementation of target detection is greatly desired. In this paper, a real-time thresholding method (RT-THRES) is proposed to obtain the constant false alarm rate (CFAR) thresholds for target detection in real-time circumstances. RT-THRES utilizes Gaussian mixture model (GMM) to track and fit the distribution of the target detector’s outputs. GMM is an extension to Gaussian probability density function, which could approximate any distribution smoothly. In this method, GMM is utilized to model the detector’s output, and then the detection threshold is calculated to achieve a CFAR detection. The conventional GMM’s parameter estimation by Expectation-Maximization (EM) requires all data samples in the dataset to be involved during the procedure and the the parameters would be re-estimated when new data samples available. Thus, GMM is difficult to be applied in real-time processing when newly observed data samples coming progressively. To improve GMM’s application availability in time-critical circumstance, an optimization strategy is proposed by introducing the Incremental GMM (IGMM) which allows GMM’s parameter to be estimated online incrementally. Experiments on real hyperspectral image and synthetic dataset suggest that RT-THRES can track and model the detection outputs’ distribution accurately which ensures the accuracy of the calculation of CFAR thresholds. Moreover, by applying the optimization strategy the computational consumption of RT-THRES maintains relatively low.