A target detection scheme for VHF SAR ground surveillance

Detection of targets concealed in foliage is a challenging problem and is critical for ground surveillance. To detect foliage-concealed targets, we need to address two major challenges, namely, 1) how to remotely acquire information that contains important features of foliage-concealed targets, and 2) how to distinguish targets from background and clutter. Synthetic aperture radar operated in low VHF-band lias shown very good penetration capability in the forest environment, and hence the first problem can be satisfactorily addressed. The second problem is the focus of this paper. Existing detection schemes can achieve good detection performance but at the cost of high false alarm rate. To address the limitation of the existing schemes, in this paper, we develop a target detection algorithm based on a supervised learning technique that maximizes the margin between two classes, i.e., the target class and the non-target class. Specifically, our target detection algorithm consists of 1) image differencing, 2) maximum-margin classifier, and 3) 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 I-RELIEF; the objective of the maximum-margin classifier is to achieve robustness against uncertainties and clutter. The diversity combining utilizes multiple images to further improve the performance of detection, and hence it is a type of multi-pass change detection. We evaluate the performance of our proposed detection algorithm, using the SAR image data collected by Swedish CARABAS-II systems which operates at low VHF-band around 20-90 MHz. The experimental results demonstrate superior performance of our algorithm, compared to the benchmark algorithm associated with the CARABAS-II SAR image data. For example, for the same level of target detection probability, our algorithm only produces 11 false alarms while the benchmark algorithm produces 86 false alarms.