Abstract:

A possibilistic K-Nearest Neighbors classifier is presented to classify mine and non-mine objects using data collected from a wideband electromagnetic induction (WEMI) sensor. The proposed classifier is motivated by the observation that buried objects often have consistent signatures depending on their metal content, size, shape, and depth. Given a joint orthogonal matching pursuits (JOMP) sparse representation, particular target types consistently selected the same dictionary elements. The proposed classifier distinguishes between target types using the frequency of dictionary elements selected by potential landmine alarms. Results are shown on data containing sixteen landmine types and several non-mine examples.

Links:

Citation:

J. Dula, A. Zare, D. Ho, and P. Gader, “Landmine classification using possibilistic K-nearest neighbors with wideband electromagnetic induction data,” in Proc. SPIE 8709 Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVIII, 2013. 

@InProceedings{dula2013landmine,
Title = {Landmine classification using possibilistic K-nearest neighbors with wideband electromagnetic induction data},
Author = {Josephine Dula and Alina Zare and Dominic Ho and Paul Gader},
Booktitle = {Proc. SPIE 8709 Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVIII},
Year = {2013},
Month = {June},
Number = {87091F},
Volume = {8709},
Doi = {10.1117/12.2016490},
}