Context-dependent classification techniques applied to landmine detection with ground-penetrating radar (GPR)
have demonstrated substantial performance improvements over conventional classification algorithms. Context-dependent
algorithms compute a decision statistic by integrating over uncertainty in the unknown, but probabilistically
inferable, context of the observation. When applied to GPR, contexts may be defined by differences
in electromagnetic properties of the subsurface environment, which are due to discrepancies in soil composition,
moisture levels, and surface texture. Context-dependent Feature Selection (CDFS) is a technique developed for
selecting a unique subset of features for classifying landmines from clutter in different environmental contexts.
In past work, context definitions were assumed to be soil moisture conditions which were known during training.
However, knowledge of environmental conditions could be difficult to obtain in the field. In this paper, we utilize
an unsupervised learning algorithm for defining contexts which are unknown a priori. Our method performs unsupervised
context identification based on similarities in physics-based and statistical features that characterize
the subsurface environment of the raw GPR data. Results indicate that utilizing this contextual information
improves classification performance, and provides performance improvements over non-context-dependent approaches.
Implications for on-line context identification will be suggested as a possible avenue for future work.