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Recent results in compressive sensing (CS)-based subsurface imaging showed that, if the target space is sparse, it can be reconstructed with many fewer number of measurements from a stepped frequency ground penetrating radar (GPR). One of the problems in this CS subsurface imaging is the surface reflections. Previous work dealed with surface reflections using a model dictionary generated from the target space excluding specifically the near surface region. While this works fine for some applications, it might lack the imaging of near surface targets. Removing the surface reflections with standard methods is not directly applicable since only very few and random measurements in the frequency domain are taken. This letter provides a simple surface reflection method using compressive measurements, that can be used for nonplanar surfaces. It is observed in both simulated and experimental GPR data that the CS-based imaging method is more robust and can find shallow targets using the surface-reflection-removed data.