Improving convolutional neural networks for buried target detection in ground penetrating radar using transfer learning via pre-training

John Bralich, Daniel Reichman, Leslie M. Collins, Jordan M. Malof

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

42 Scopus citations

Abstract

The Ground Penetrating Radar (GPR) is a remote sensing modality that has been used to collect data for the task of buried threat detection. The returns of the GPR can be organized as images in which the characteristic visual patterns of threats can be leveraged for detection using visual descriptors. Recently, convolutional neural networks (CNNs) have been applied to this problem, inspired by their state-of-the-art-performance on object recognition tasks in natural images. One well known limitation of CNNs is that they require large amounts of data for training (i.e., parameter inference) to avoid overfitting (i.e., poor generalization). This presents a major challenge for target detection in GPR because of the (relatively) few labeled examples of targets and non-target GPR data. In this work we use a popular transfer learning approach for CNNs to address this problem. In this approach we train two CNN on other, much larger, datasets of grayscale imagery for different problems. Specifically, we pre-train our CNNs on (i) the popular Cifar10 dataset, and (ii) a dataset of high resolution aerial imagery for detecting solar photovoltaic arrays. We then use varying subsets of the parameters from these two pre-trained CNNs to initialize the training of our buried threat detection networks for GPR data. We conduct experiments on a large collection of GPR data and demonstrate that these approaches improve the performance of CNNs for buried target detection in GPR data.

Original languageEnglish
Title of host publicationDetection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII
EditorsSteven S. Bishop, Jason C. Isaacs
PublisherSPIE
ISBN (Electronic)9781510608658
DOIs
StatePublished - 2017
EventDetection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII 2017 - Anaheim, United States
Duration: Apr 10 2017Apr 12 2017

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume10182
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceDetection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII 2017
Country/TerritoryUnited States
CityAnaheim
Period04/10/1704/12/17

Keywords

  • Buried threat detection
  • Convolutional neural network
  • Ground penetrating radar
  • Transfer learning

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