Some good practices for applying convolutional neural networks to buried threat detection in Ground Penetrating Radar

@article{Reichman2017SomeGP,
  title={Some good practices for applying convolutional neural networks to buried threat detection in Ground Penetrating Radar},
  author={Danix00EBl Reichman and Leslie M. Collins and Jordan M. Malof},
  journal={2017 9th International Workshop on Advanced Ground Penetrating Radar (IWAGPR)},
  year={2017},
  pages={1-5}
}
Ground Penetrating Radar (GPR) is a remote sensing modality that has been researched extensively for buried threat detection. For this purpose, algorithms can be developed to automatically determine the presence of such threats. To train such algorithms, small 2-dimensional images can be extracted from the larger image, or volume, of GPR data. One thread of research in the buried threat detection literature is to use visual descriptors from the computer vision literature. One recent, very… CONTINUE READING

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