Classification of explosives materials detected by magnetic anomaly method

Abstract

In this study, it is aimed to classify buried explosives detected by magnetic anomaly method by means of nearest neighborhood classification algorithm. In this context, buried object data acquisition system using passive measurement technique has been used to scan 10 explosive samples having ferromagnetic properties and 10 misleading materials having almost similar geometry with explosives. Scanning procedures for all samples have been made at 5 cm, 10 cm, and 15 cm distances from the top of the scanned object. As a result, a total of 60 data matrices of 32x25 dimensions have been obtained. The classification has been carried out using the nearest neighborhood algorithm just after attribute extractions were performed for the data matrices. The classification results for samples were compared using the obtained attributes and neighborhood values. In the classification, 91.66% success has been achieved by using the standard deviation values, Kurtosis Coefficient values arithmetic mean attributes and by taking into account k=3 nearest neighborhood.