改进的K均值聚类红外目标检测方法

IR target detection based on improved K-means clustering

利用图像方差能很好地反映目标边缘信息的特点,提出一种基于方差的K均值聚类红外目标检测算法。利用形态学方法对红外图像进行预处理,运用相应的模板计算得到红外图像的方差图像,利用K均值聚类算法对方差图像进行聚类,从而分离出目标类别和背景类别。实验表明,该算法提取的红外图像中目标信息的兰德指数最高,说明该算法能有效地提取红外图像中目标信息,从而达到目标检测的目的。

Considering the variance of image was a very good response for edge information, a target detection algorithm by K-means clustering algorithm based on variance was presented. First, this paper prepressed the infrared image by morphological method, and calculated the corresponding variance image by using a specific template, then gathered each difference image class by using the K-means clustering method, finally the different target edge information was got. Experimental results show that the algorithm can effectively extract the IR target edge.