红外多光谱图像弹道目标检测算法  

Ballistic target detection in infrared multispectral imagery

作　　者：黄树彩[1] 凌强[1] 韦道知[1] 吴潇[1] 

机构地区：[1]空军工程大学防空反导学院,陕西西安710051

出　　处：《国防科技大学学报》2016年第6期142-147,共6页Journal of National University of Defense Technology

基　　金：航空科学基金资助项目(20130196004)

摘　　要：在红外多光谱图像中,弹道导弹尾焰拥有两大特征,一是由强烈红外辐射引起的灰度差异,二是独特的光谱特性。然而,传统的单波段检测技术只利用了尾焰强烈的辐射特性,而近些年发展起来的多光谱检测技术则只利用了尾焰独特的光谱特性。为了充分利用导弹尾焰的两大特征,将单波段检测技术和多光谱检测技术结合起来,提出三种检测算法,并从算法的检测效果、运算量和鲁棒性三方面详细分析它们的优缺点。采用人工合成的红外多光谱图像进行验证,实验结果表明,相比单独使用单波段或多光谱的检测算法,融合算法的检测性能更好。There are two important characteristics for missile plume in infrared multispectral imagery： one is the gray-scale difference caused by strong infrared radiance; and the other one is the unique spectral signature feature. However, the classical single-band detection technology only uses the first characteristic, and the multispectral detection technology which has been developed in recent years only uses the second characteristic. In order to fully exploit the characteristics of missile plume, three detection algorithms were proposed by combining the single-band and multispectral detection technology. The advantages and disadvantages of the three algorithms were discussed in detail in the aspects of detection performance, computational complexity and robustness. Experiments on synthetic infrared multispectral imagery demonstrate a better performance of the combined algorithms when compared with single-band or multispectral detection algorithm.

关 键 词：弹道目标检测 单波段目标检测 多光谱目标检测 红外多光谱图像 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置] TP391[自动化与计算机技术—控制科学与工程]