Detection and discrimination of land mines in ground-penetrating radar based on edge histogram descriptors

This paper describes an algorithm for land mine detection in GPR data that uses edge histogram descriptors for feature extraction and fuzzy K-Nearest Neighbors (K-NN) for confidence assignment. First, an LMS algorithm for anomaly detection is used to focus attention and identify candidate signatures that resemble mines. Second, translation invariant features are extracted based on spatial distribution of edges in the 3-D GPR signatures. Specifically, each 3-D signature is divided into sub-signatures, and the local edge distribution for each sub-signature is represented by a histogram. To generate the histogram, local edges are categorized into five types: vertical, horizontal, diagonal, anti-diagonal, and non-edges. Next, the training signatures are clustered to identify prototypes. The main idea is to identify few prototypes that can capture the variations of the signatures within each class. These variations could be due to different mine types, different soil conditions, different weather conditions, etc. Fuzzy memberships are assigned to these representatives to capture their degree of sharing among the mines and false alarm classes. Finally, fuzzy K-NN based rules are used to assign a confidence value to distinguish true detections from false alarms. The proposed algorithm is applied to data acquired from three outdoor test sites at different geographic locations.