基于多时相红外图像探测浅层地下目标

DETECTION OF BURIED TARGETS BASED ON MULTITEMPORAL INFRARED IMAGE

因浅层地下目标的存在引起地表红外图像随时间变化,通过多时相红外图像探测了地下目标.针对部分红外图像中的曝光噪声等干扰会造成多时相红外图像自动选取的困难,提出了基于核小成分分析的多时相去噪方法,自动获得效果良好的图像;然后采用基于空间和时相变化信息约束的多时相模糊核聚类算法对去噪后的多时相红外图像进行分类,其中引入了时相信息指数,对时相权重因子进行修正;最后由分类结果给出符合逻辑的地下目标的位置及大致种类数,并由地下目标的红外成像机理初步给出地下目标的大体物理性质,为利用热红外图像探测地下目标提供了一些有意义的研究.

Since the existence of buried targets influences the surface＇ IR images changing with time, buried targets were detected by multitemporal infrared images. In order to denoise the exposal nioses of partial IR images, a method of kernel minor component analysis （KMCA） was put forward to denoise muhitemporal images and overcome the difficulty in choosing images automatically, thus, the good images could be attained automatically. Then a fuzzy kernel cluster algorithm with spatio and temporal restrictions （STKFCM） was proposed to detect buried targets based on multitemporal IR images, in which the index of temporal information was introduced to modify the temporal weight factor. At last, the position of targets and the number of classes were estimated by the results of classification. After taking the mechanism of buried targets IR images into account, the general characters of targets were obtained. This is an interesting study to detect buried targets by using the technology of IR.