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A vehicle-mounted forward-looking ground-penetrating radar (GPR) with multiple-input and multiple-output (MIMO) array can obtain the high-resolution image of its front area to perform the standoff detection of landmines. The major challenge for the GPR landmine detection over wide areas is the very high false alarm rate when maintaining a high detection probability. In this paper, a novel feature extraction method is proposed to obtain the bistatic scattering information from the MIMO array image to discriminate landmines from clutter. To realize the goal, an imaging model of the MIMO array is firstly developed. Based on the imaging model, the bistatic scattering function of a suspected object is estimated from its MIMO array image using the space-wavenumber processing. Images of different incident angles and bistatic angles at some resonance frequencies are selected from the estimated bistatic scattering function to represent the scattering characteristics. In order to obtain the scale, rotation, and translation invariant feature vector, Hu moment invariants of the selected images are calculated to form the low-dimensional feature vector. The experimental results show that the proposed method can offer an efficient feature vector for the landmine discriminator to improve the detection performance.