Abstarct: Ground Penetrating Radar (GPR) is the general term applied to techniques which employ radio waves, typically in Mega Hertz and Giga Hertz range, to map structures and features buried in the ground. GPR measurements can suffer from large amount of noise. This primarily caused by interference from other radio wave emitting devices that are present in the surrounding area of the GPR system during data collection. In addition to noise, presence of clutter, reflections from other non-target objects buried underground in the vicinity of the target can make GPR measurement difficult to understand and interpret. Also, the clutter can be caused by direct coupling between transmitting and receiving antennas and reflection from the ground. In the case of landmine detection, since targets are located near the surface, a target signal may be completely covered by the clutter. Thus, clutter reduction must be performed prior to any target detection scheme in the GPR. This thesis is concerned with methods that can be applied to GPR data in order to enhance target detection performance. These methods include singular value decomposition (SVD), principal component analysis (PCA), independent component analysis (ICA) and morphological component analysis (MCA). Simulated data with GPRMax software is used for evaluation methods. The peak signal to noise ratio (PSNR) scores for SVD, PCA, FASTICA, JADE, MCA methods respectively are 32.8, 33.2, 33.1, 37.2 and 45.4 (dB). Also, the MCA method has best result for GPR images contain more than on targets.