Parametric Adaptive Signal Detection for Hyperspectral Imaging

In this paper, we introduce a class of training-efficient adaptive signal detectors that exploit a parametric model taking into account the non-stationarity of HSI data in the spectral dimension. A maximum likelihood (ML) estimator is presented for estimation of the parameters associated with the proposed parametric model. Several important issues are discussed, including model order selection, training screening, and time-series based whitening and detection, which are intrinsic parts of the proposed parametric adaptive detectors. Experimental results using real HSI data reveal that the proposed parametric detectors are more training-efficient and outperform conventional covariance-matrix based detectors when the training size is limited