Spatial and temporal features selection for low-altitude target detection

Target detection in plane position indicator (PPI) radar images aims at separating moving targets from complicated background image. Background subtraction is a powerful mechanism for such applications. Since there is still much clutter left in the foreground image after background subtraction, an optimal classification plane (OCP) should be constructed to distinguish targets from clutters. Due to the complexity and variability of the foreground and background images, the threshold value of each position of the OCP should be selected adaptively corresponding to each pixel of the foreground image. In this paper, a novel method is proposed to improve the classification results of the OCP with the spatial and temporal features from the PPI radar image sequence. Firstly, to select the thresholds in the OCP adaptively, a new formula is developed with the spatial features from two statistical models. The statistics from the foreground model reflect the aggregation degree of the concerned pixels, while those from the background model reflect their relative positions. Secondly, to further reduce the false alarm rate, a novel strategy based on the temporal features is incorporated to modify the OCP. Our method with the optimal parameter values is compared with the other successful techniques for target detection. Quantitative evaluations show that the proposed method provides better results.