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Detection of unexploded ordnance via efficient semisupervised and active learning

Publication ,  Journal Article
Liu, Q; Liao, X; Carin, L
Published in: IEEE Transactions on Geoscience and Remote Sensing
September 1, 2008

Semisupervised learning and active learning are considered for unexploded ordnance (UXO) detection. Semisupervised learning algorithms are designed using both labeled and unlabeled data, where here labeled data correspond to sensor signatures for which the identity of the buried item (UXO/non-UXO) is known; for unlabeled data, one only has access to the corresponding sensor data. Active learning is used to define which unlabeled signatures would be most informative to improve the classifier design if the associated label could be acquired (where for UXO sensing, the label is acquired by excavation). A graph-based semisupervised algorithm is applied, which employs the idea of a random Markov walk on a graph, thereby exploiting knowledge of the data manifold (where the manifold is defined by both the labeled and unlabeled data). The algorithm is used to infer labels for the unlabeled data, providing a probability that a given unlabeled signature corresponds to a buried UXO. An efficient active-learning procedure is developed for this algorithm, based on a mutual information measure. In this manner, one initially performs excavation with the purpose of acquiring labels to improve the classifier, and once this active-learning phase is completed, the resulting semisupervised classifier is then applied to the remaining unlabeled signatures to quantify the probability that each such item is a UXO. Example classification results are presented for an actual UXO site, based on electromagnetic induction and magnetometer data. Performance is assessed in comparison to other semisupervised approaches, as well as to supervised algorithms. © 2008 IEEE.

Duke Scholars

Published In

IEEE Transactions on Geoscience and Remote Sensing

DOI

ISSN

0196-2892

Publication Date

September 1, 2008

Volume

46

Issue

9

Start / End Page

2558 / 2567

Related Subject Headings

  • Geological & Geomatics Engineering
  • 40 Engineering
  • 37 Earth sciences
  • 0909 Geomatic Engineering
  • 0906 Electrical and Electronic Engineering
  • 0404 Geophysics
 

Citation

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Liu, Q., Liao, X., & Carin, L. (2008). Detection of unexploded ordnance via efficient semisupervised and active learning. IEEE Transactions on Geoscience and Remote Sensing, 46(9), 2558–2567. https://doi.org/10.1109/TGRS.2008.920468
Liu, Q., X. Liao, and L. Carin. “Detection of unexploded ordnance via efficient semisupervised and active learning.” IEEE Transactions on Geoscience and Remote Sensing 46, no. 9 (September 1, 2008): 2558–67. https://doi.org/10.1109/TGRS.2008.920468.
Liu Q, Liao X, Carin L. Detection of unexploded ordnance via efficient semisupervised and active learning. IEEE Transactions on Geoscience and Remote Sensing. 2008 Sep 1;46(9):2558–67.
Liu, Q., et al. “Detection of unexploded ordnance via efficient semisupervised and active learning.” IEEE Transactions on Geoscience and Remote Sensing, vol. 46, no. 9, Sept. 2008, pp. 2558–67. Scopus, doi:10.1109/TGRS.2008.920468.
Liu Q, Liao X, Carin L. Detection of unexploded ordnance via efficient semisupervised and active learning. IEEE Transactions on Geoscience and Remote Sensing. 2008 Sep 1;46(9):2558–2567.

Published In

IEEE Transactions on Geoscience and Remote Sensing

DOI

ISSN

0196-2892

Publication Date

September 1, 2008

Volume

46

Issue

9

Start / End Page

2558 / 2567

Related Subject Headings

  • Geological & Geomatics Engineering
  • 40 Engineering
  • 37 Earth sciences
  • 0909 Geomatic Engineering
  • 0906 Electrical and Electronic Engineering
  • 0404 Geophysics