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Classification of GPR data for mine detection based on hidden Markov models

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2 Author(s)
Lohlein, O. ; Res. & Technol., Daimler-Benz AG, Ulm, Germany ; Fritzsche, M.

We present a novel approach for the classification of GPR data, based on hidden Markov models. It assumes that the system, generating the recorded data, can be in one of a set of distinct states. At discrete intervals, given by the distance between the recording positions of two adjacent radar scans, the system can either undergo a change of state or remain in the same state, according to a set of probabilities assigned to the allowed transitions between states. The appeal of the method is that it is not restricted to a classification on a scan-by-scan basis, but that it allows one to look at a sequence of data of a certain lateral extension. This approach can thus accommodate characteristic object pattern evolving not only in time, but also in space. Our results indicate that HMMs outperform scan-wise classification, based on alternative algorithms, such as polynomial classifiers, neural or radial basis function networks

Published in:

Detection of Abandoned Land Mines, 1998. Second International Conference on the (Conf. Publ. No. 458)

Date of Conference:

12-14 Oct 1998

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