TY - GEN

T1 - Detection of unexploded ordnance via efficient semisupervised and active learning

AU - Liu, Qiuhua

AU - Liao, Xuejun

AU - Carin, Lawrence

N1 - Generated from Scopus record by KAUST IRTS on 2021-02-09

PY - 2008/9/1

Y1 - 2008/9/1

N2 - 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.

AB - 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.

UR - http://ieeexplore.ieee.org/document/4588242/

UR - http://www.scopus.com/inward/record.url?scp=50549095916&partnerID=8YFLogxK

U2 - 10.1109/TGRS.2008.920468

DO - 10.1109/TGRS.2008.920468

M3 - Conference contribution

SP - 2558

EP - 2567

BT - IEEE Transactions on Geoscience and Remote Sensing

ER -