Parametric adaptive signal detection for hyperspectral imaging

Hyperspectral imaging (HSI) sensors can provide very fine spectral resolution that allows remote identification of ground objects smaller than a full pixel in an HSI image. Traditional approaches to the so-called subpixel target signal detection problem are training inefficient due to the need for an estimate of a large-size covariance matrix of the background from target-free training pixels. This imposes a training requirement that is often difficult to meet in a heterogeneous environment. In this paper, a class of training-efficient adaptive signal detectors is presented by exploiting a parametric model that takes into account the nonstationarity of HSI data in the spectral dimension. A maximum-likelihood (ML) estimator is developed to estimate 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