Target detection for very high-frequency synthetic aperture radar ground surveillance

A target detection algorithm is developed based on a supervised learning technique that maximises the margin between two classes, that is, the target class and the non-target class. Specifically, the proposed target detection algorithm consists of (i) image differencing, (ii) maximum-margin classifier, and (iii) diversity combining. The image differencing is to enhance and highlight the targets so that the targets are more distinguishable from the background. The maximum-margin classifier is based on a recently developed feature weighting technique called Iterative RELIEF; the objective of the maximum-margin classifier is to achieve robustness against uncertainties and clutter. The diversity combining utilises multiple images to further improve the performance of detection, and hence it is a type of multi-pass change detection. The authors evaluate the performance of the proposed detection algorithm, using the CARABAS-II synthetic aperture radar (SAR) image data and the experimental results demonstrate superior performance of the proposed algorithm, compared to the benchmark algorithm. © 2012 The Institution of Engineering and Technology.