Rayleigh商二次相关滤波器(RQQCF)是一种重要的目标检测方法, 但其直接对原始图像数据进行操作, 目标检测效果不总是很理想. 核方法可以描述图像的高阶统计特性, 有效抑制噪声及杂波, 提高目标检测效果.将Rayleigh商二次相关滤波器映射到高维核特征空间, 完整推导核Rayleigh商二次相关滤波器(KRQQCF), 提出用核特征提取方法解决其实现问题, 并将算法用到红外目标检测中. 实验中以核主成分分析(KPCA)特征提取方法为例, 用真实场景下采集的红外数据对KRQQCF的性能进行检验, 结果表明KRQQCF的性能明显高于RQQCF, 该方法能实现对红外目标的准确检测, 是一种有效的红外目标检测方法.

Rayleigh quotient quadratic correlation filter (RQQCF) is an important technique for target detection. Since it operates directly on image data, satisfying results can’t be always achieved when it is used in infrared target detection. Higher-order statistical properties of the image can effectively suppress the noise and clutter and improve the detection results which can be realized by means of kernel method in kernel space. In this paper, kernel Rayleigh quotient quadratic correlation filter (KRQQCF) was developed by extending RQQCF to the higher-dimensional space, i.e., the kernel space. Though the derivation was completed, this kernel filter couldn’t be achieved directly. Kernel feature extraction method was proposed to settle this problem. The algorithm was used to detect infrared targets, and kernel principal component analysis(KPCA) was adopted to obtain this KRQQCF in experiments. Experimental results using real-life infrared images confirm the excellent performance of KRQQCF, and that KRQQCF outperforms RQQCF significantly in infrared target detection. Consequently, KRQQCF is an effective method for infrared target detection and can achieve accurate detection results.