Adaptive target detection in hyperspectral imaging from two sets of training samples with different means

In this paper, we consider local detection of a target in hyperspectral imaging and we assume that the spectral signature of interest is buried in a background which follows an elliptically contoured distribution with unknown parameters. In order to infer the background parameters, two sets of training samples are available: one set, taken from pixels close to the pixel under test, shares the same mean and covariance while a second set of farther pixels shares the same covariance but has a different mean. When the whole data samples (pixel under test and training samples) follow a matrix-variate t distribution, the one-step generalized likelihood ratio test (GLRT) is derived in closed-form. It is shown that this GLRT coincides with that obtained under a Gaussian assumption and that it guarantees a constant false alarm rate. We also present a two-step GLRT where the mean and covariance of the background are estimated from the training samples only and then plugged in the GLRT based on the pixel under test only. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

This paper explores local detection of a target in hyperspectral imaging using an elliptically contoured background distribution and matrix-variate t distribution for parameter inference. It presents one-step and two-step generalized likelihood ratio tests, showing that the former coincides with the Gaussian assumption and maintains a constant false alarm rate.