  Detection of a target with known spectral signature when this target may
occupy only a fraction of the pixel is an important issue in hyperspectral
imaging. We recently derived the generalized likelihood ratio test (GLRT) for
such sub-pixel targets, either for the so-called replacement model where the
presence of a target induces a decrease of the background power, due to the sum
of abundances equal to one, or for a mixed model which alleviates some of the
limitations of the replacement model. In both cases, the background was assumed
to be Gaussian distributed. The aim of this short communication is to extend
these detectors to the broader class of elliptically contoured distributions,
more precisely matrix-variate $t$-distributions with unknown mean and
covariance matrix. We show that the generalized likelihood ratio tests in the
$t$-distributed case coincide with their Gaussian counterparts, which confers
the latter an increased generality for application. The performance as well as
the robustness of these detectors are evaluated through numerical simulations.
