A Large Comparison of Feature-Based Approaches for Buried Target Classification in Forward-Looking Ground-Penetrating Radar

@article{Camilo2018ALC,
  title={A Large Comparison of Feature-Based Approaches for Buried Target Classification in Forward-Looking Ground-Penetrating Radar},
  author={Joseph A. Camilo and Leslie M. Collins and Jordan M. Malof},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  year={2018},
  volume={56},
  pages={547-558}
}
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 are beamformed into images, and then, computerized algorithms are applied to automatically detect subsurface buried targets. Most existing algorithms are supervised, meaning that they are trained to discriminate between labeled target and nontarget imagery, usually based on features extracted from the imagery… CONTINUE READING
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