Detection of buried landmines and other explosive objects using ground penetrating radar (GPR) has been investigated for almost two decades and several classifiers have been developed. Most of these methods are based on the supervised learning paradigm where labeled target and clutter signatures are needed to train a classifier to discriminate between the two classes. Typically, large and diverse labeled training samples are needed to improve the performance of the classifier by overcoming noise and adding robustness and generalization to unseen examples. Unfortunately, even though unlabeled GPR data may be abundant, labeled data are often available in small quantities as the labeling process is tedious and can be ambiguous for most of the data. In this paper, we propose an algorithm for detecting landmines and buried objects that uses unlabeled data to help labeled data in the classification process. Our algorithm is graph-based and propagates the nodes labels to neighboring nodes according to their proximity in the feature space. For labeled data, we use a set of prototypes that are extracted from a small set of labeled training samples. For unlabeled data, we use a collection of signatures that are extracted from the vicinity of the alarm being tested. This choice is based on the assumption that many spatially close signatures are expected to have similar features and thus, unlabeled samples can create dense regions that link different regions of the labeled samples and propagate their labels to test samples. In other words, unlabeled samples are explored to create a context for each test alarm. To validate the proposed label propagation based classifier, we use it to detect buried explosive objects in GPR data collected by an experimental hand held demonstrator. We show that our approach is robust and computationally efficient to be used for both target discrimination and prescreening.