能量累积与均值漂移聚类结合的红外小目标检测方法

Small Target Detection Based on Method Energy Accumulation and Mean Shift Clustering in Infrared Image

研究红外图像中弱小目标的检测问题,提出了一种基于能量累积与均值漂移聚类的红外小目标检测方法。首先利用滑动窗口处理实现图像序列中目标能量积累,去除图像中的随机噪声,提高信噪比;然后选用不同尺度的核带宽对原始图像进行两次均值漂移聚类滤波,将两次滤波所得的图像灰度进行差分来实现背景抑制,提高目标与背景的对比度;之后根据图像的统计特性确定阈值,用门限分割方法提取目标区域,实现候选目标的准确定位;最后利用序列图像中目标运动的连续性和轨迹的一致性筛选出真正的目标。该算法通过选用不同尺度的核带宽进行聚类来得到目标及背景图像,背景抑制的同时也滤除了噪声。实验结果表明,该方法能快速有效地提取复杂背景条件下的红外小目标。

A new small target detection method for infrared image based on energy accumulation and mean shift clustering is presented. Firstly, accumulating the energy of the infrared image sequence in the sliding window with fixed size can remove the random noise in the infrared images to increase the SNR （signal to noise ratio）. Secondly, we suppress the background by using the difference of adaptive mean shift filtering results with two different bandwidths. And thirdly, we segmenting the regions where there are possible targets and extract the statistical features. Finally filter the true targets according to the principle of moving continuity and trajectory consistency of moving target in the image sequences. Target image and background image can be obtained by use of differernt mean shift clustering, the noise can be excluded at the same time. The experimental results indicate that the method can effectively detect the small and weak targets under complex backgrounds.