A large comparison of feature-based approaches for buried target classification in forward-looking ground-penetrating radar

TL;DR

This paper compares various feature-based methods for buried target detection in forward-looking ground-penetrating radar, demonstrating that modern feature learning approaches outperform traditional features, with limited gains from fusion.

Contribution

It provides a comprehensive comparison of existing features and introduces modern feature learning methods like bag-of-visual-words and Fisher vectors for FLGPR.

Findings

Modern feature learning methods outperform traditional features.

Fusion of features yields limited additional improvements.

New approaches show significant performance gains.

Abstract

Forward-looking ground-penetrating radar (FLGPR) has recently been investigated as a remote sensing modality for buried target detection (e.g., landmines). In this context, raw FLGPR data is beamformed into images and then computerized algorithms are applied to automatically detect subsurface buried targets. Most existing algorithms are supervised, meaning they are trained to discriminate between labeled target and non-target imagery, usually based on features extracted from the imagery. A large number of features have been proposed for this purpose, however thus far it is unclear which are the most effective. The first goal of this work is to provide a comprehensive comparison of detection performance using existing features on a large collection of FLGPR data. Fusion of the decisions resulting from processing each feature is also considered. The second goal of this work is to investigate two modern feature learning approaches from the object recognition literature: the bag-of-visual-words and the Fisher vector for FLGPR processing. The results indicate that the new feature learning approaches outperform existing methods. Results also show that fusion between existing features and new features yields little additional performance improvements.