Learning an object category from only a few samples is generally not adequate for correct classification. One needs many training samples to obtain a classifier that generalizes well and that has sufficient success rates. However, in several applications, including target detection from Ground Penetrated Radar (GPR) data, collecting many annotated data is not always possible. In a GPR data collection, the images formed show a nonlinear dependence on the soil properties such as the permeability and permittivity. Therefore, even if enough training data were available to train a good classifier for one soil type (such as dry sand); the success of this classifier does not translate well if the soil type is changed (say, to wet sand).