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Buried object classification using holographic radar

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The ability of RASCAN holographic radar to distinguish buried objects through their shape and texture has been investigated. RASCAN produces data that can be processed into a two-dimensional subsurface image suitable for object identification either by eye or by computer, where scanned receptive fields can be used for object location and trained neural networks for object identification. With the eventual objective of distinguishing buried antipersonnel landmines from battlefield clutter, the five objects considered were: a simulated mine, a small unexploded shell, a crushed aluminium can, a short length of barbed wire and a stone. In the first experiments, the objects were buried in fine, dry sand so that the object classification methods could be tested in the absence of the inevitable false alarm features arising from rough and uneven surfaces and soil inhomogeneity. Training data were collected from 11 scans, each containing these five objects at random positions and depths. The simulated mines were identified with 100% success, with zero false alarms in both training and testing. The clutter test objects were identified with around a 75% success rate and about 15% false alarms. An unseen validation image correctly identified the mine and three of the four clutter objects.
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Keywords: CLUTTER; DEMINING; GPR; GROUND PENETRATING RADAR; HOLOGRAPHIC RADAR; NEURAL NETWORKS

Document Type: Research Article

Publication date: June 1, 2012

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