低对比度小目标检测

对强杂波背景下的远距离目标探测,提出基于序列图像的局部自适应背景预测,获得图像背景的最佳估计。对残差图像采用能量累积及中值滤波消除背景杂波。为提高信噪比,采用带缓冲窗口的双窗滤波法使目标和背景的差别更加显著,有利于低对比度下的目标分割。最后采用改进的高阶相关方法,在不影响检测性能的情况下加快了真实目标识别的运算收敛速度,并最终实现了算法工程化,在图像局部信噪比大于0.3时,采用三阶相关时检测概率达到98%。

Detection of dim point target with low contrast

An image sequence-based method is proposed for target detection within a large-field bright background from a long distance. Optimal estimation of image background is obtained using adaptive prediction of local background. For the image with background subtracted, energy accumulation and median filter are used to further remove clutter and to improve signal-to-noise ratio (SNR). Then the targets are located by double window filtering with buffer window which further segments the targets from background. With improved high order correlation, the true dim target is eventually detected with a small amount of calculations. The proposed method is implemented in field experiments. Using three order correlation, its detection probability achieves 98% when the local SNR is higher than 0.3.