Most of the existing ground-penetrating radar-based handheld landmine detectors use algorithms with low computational complexity to obtain detection results quickly. However, these algorithms do not show robust performance against background signals, such as noise signal, shake of sensor, and fluctuation of the signal. To ensure robustness against various background signals, a detection method using a model considering the statistical characteristics of signals should be used. Principal component analysis (PCA), which is a representative method, showed excellent performance in preprocessing and detection steps in the field of landmine detection. However, when the number of training data is small, the performance of PCA cannot be guaranteed. In addition, the amount of computation is increased because of high-dimensional data. Therefore, in this study, we propose a bilateral 2-D-discrete cosine transform PCA (B2D-DCT-PCA) method that shows robust performance with a small number of training samples and reduces computational complexity. In addition, landmine-background reconstruction error (LBRE) is introduced as an index for evaluating how well the difference between landmine and its background is distinguished to determine a suitable preprocessing method for the proposed method, and LBREs according to various preprocessing methods are compared. As a result, the frequency shifting showing the best performance is used. After optimizing the parameters of the proposed method, B2D-DCT-PCA, we confirm that it shows better performance compared with the conventional methods.