This paper addresses the detection of mine-like objects in stepped-frequency ground penetrating radar (SF-GPR) data as a function of object size, object content, and burial depth. The detection approach is based on a Selective Independent Component Analysis (SICA). SICA provides an automatic ranking of components, which enables the suppression of clutter, hence extraction of components carrying mine information. The goal of the investigation is to evaluate various time and frequency domain ICA approaches based on SICA. The performance comparison is based on a series of mine-like objects ranging from small-scale anti-personal (AP) mines to large-scale anti-tank (AT) mines. Large-scale SF-GPR measurements on this series of mine-like objects buried in soil were performed. The SF-GPR data was acquired using a wideband monostatic bow-tie antenna operating in the frequency range 750 MHz - 3.0 GHz. The detection and clutter reduction approaches based on SICA are successfully evaluated on this SF-GPR dataset.