A small-target detector based on single likelihood test for hyperspectral imagery is presented to detect target when there is no a priori spectral signal of background and target, which presume the maximum entropy character of target. Because of the low-probability occurrence of target compared with that of background, it can be assumed that there is no constraint by hyperspectral imagery data on the moments of target signal. Accordingly, the generalize likelihood ratio test can be simplified to test background likelihood solely under maximum entropy of target. Then, nonparametric estimation is utilized to obtain the probability density of background, which can extract information from samples more effectively. The single likelihood test based detector weakens the effect of statistic model discrepancy and avoids effect of implicit physical meaning on detection. Theoretic analysis and the experimental results on visible/near-infrared OMIS-I hyperspectral imagery verify that these algorithms are effective to detect spatial low-probability targets.