This paper proposes a Synthetic Aperture Radar (SAR) vehicle target detection algorithm based on contextual knowledge. The proposed algorithm firstly obtains the general classification of SAR image with a Markov Random Field (MRF)-based segmentation algorithm; then modifies the prior target presence probability utilizing terrain types, distances to boundary and target aggregation degree; finally gains the detection results using improved Cell Averaging-Constant False Alarm Rate (CA-CFAR). Detections with real SAR image data show that this algorithm can effectively improve target detection rate and reduce false alarms compared with conventional CA-CFAR.
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