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Infrared polarisation measurements of surface and buried anti-personnel landmines

Author: Cremer, F. · Jong, W. de · Schutte, K.
Publisher: SPIE
Place: Bellingham, WA
Institution: TNO Fysisch en Elektronisch Laboratorium
Source:Dubey A.C.Harvey J.F.Broach, J.T.George, V., Detection and remediation technologies for mines and minelike targets VI, 16-20 April 2001, Orlando, FL, USA, 164-175
Proceedings of SPIE
Identifier: 95407
doi: doi:10.1117/12.445468
Keywords: Defence · Landmine detection · Measurements · Infrared imaging · Light polarization · Mathematical models · Synchronization · Anti-personnel mines · Mines · Buried objects


Linear polarisation of Thermal InfraRed (TIR) radiation occurs whenever radiation is reflected or emitted from a smooth surface (such as the top of a landmine) and observed from a grazing angle. The background (soil and vegetation) is generally much rougher and therefore has less pronounced linear polarised radiation. This difference in polarisation can be used to enhanced detection of land mines using TIR cameras. A measurement setup is constructed for measurement of polarised TIR images. This setup contains a rotating polarisation filter which rotates synchronously with the frame sync of the camera. Either a Long Wave InfraRed (LWIR) or a Mid Wave InfraRed (MWIR) camera can be mounted behind the rotating polarisation filter. The synchronisation allows a sequence of images to be taken with a predefined constant angle of rotation between the images. Out of this image sequence three independent Stokes images are calculated, consisting of the unpolarised part, the vertical/horizontal polarisations and the two diagonal polarisations. An initial model is developed that describes the polarisation due to reflection of and emission from a smooth surface. This model predicts the linear polarisation for a landmine 'illuminated' by a source that is either hotter or cooler than the surface of them landmine. The measurement setup is used indoors to validate the model. The measurements agree well with the model predictions.