In this paper, we propose a sample selection method for compressive multiple-input multiple-output (MIMO) ultra-wideband (UWB) noise radar imaging. The proposed sample selection is based on comparing norm values of the transmitted sequences, and selects the largest M samples among N candidates per antenna. Moreover, we propose an adaptive weight allocation which improves normalized mean-square error (NMSE) by maximizing the mutual information between target echoes and the transmitted signals. Further, this weighting scheme is applicable to both sample selection schemes, a conventional random sampling and the proposed selection. Simulations show that the proposed selection method can improve the multiple target detection probability and NMSE. Moreover, the proposed weight allocation scheme is applicable to those selection methods and obtains spatial diversity and signal-to-noise ratio (SNR) gains.