A training sample selection method is proposed by fusing the generalized inner product (GIP) statistic with geography information to construct the fused metric of the sample set. Based on this metric, more suitable training samples are selected. The simulation results demonstrate that the proposed method exhibits excellent robustness in different clutter environments, particularly in complex ground environments containing discrete moving scatterers, where its clutter suppression performance is better than that of conventional methods.