Computational Intelligence Techniques in Landmine Detection

Abstract

Knowledge-based techniques have been used to automatically detect surface land mines present in thermal and multispectral images. Polarisation sensitive infrared sensing is used to highlight the polarisation signature of man-made targets such as landmines over natural features in the image. Processing the thermal polarisation images using a background discrimination algorithm we were able to successfully identify eight of the nine man-made targets, three of which were mines with only three false targets. A digital camera was used to collect a number of multispectral bands of the test mine area containing three surface landmines with natural and man-made clutter. Using a supervised and unsupervised neural network technique on the textural and spectral characteristics of selected multispectral bands we successfully identified the three surface mines but obtained numerous false targets with varying degrees of accuracy. Finally to further improve our detection of land mines we use a fuzzy rule based fusion technique on the processed polarisation resolved image together with the output results of the two best classifies. Fuzzy rule based fusion identified the locations of all three landmines and reduced the false alarm rate from seven (as obtained by the polarisation resolved image) to two.KeywordsFalse AlarmFuzzy RuleFalse Alarm RateInformation Processing TechniqueNeural Network ClassifierThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.