Singular value decomposition (SVD), principal component analysis (PCA), and independent component analysis (ICA) are used as subspace based methods and curvelet transform (CT), nonsubsampled contourlet transform (NSCT) are used as multi-resolution based methods for clutter reduction algorithms in Ground-Penetrating Radar (GPR) images have been proposed in recent years with demonstrated success. However, the datasets for evaluated clutter reduction algorithms are not the same and using different setups. Thus, the performance result of algorithms in the literature are incomparable and sometimes contradictory. To address these problems, we design an extensive evaluation for the wellknown clutter reduction algorithms with various scenarios to understand how these methods perform within the same framework. The methods are evaluated on the simulated data generated by the GprMax program. A large library of simulated data is constructed by changing the three crucial parameters such as soil types, burial depths and material types in order to analyze the methods in depth. The performance of both groups of methods are evaluated and results are reported in the sense of peak signal-to-noise (PSNR) for various scenarios.