中文摘要：

提出了一种新的基于混合核函数的加权RX算法,用于高光谱图像异常检测.在将原始高光谱数据非线性映射到高维特征空间以挖掘高光谱图像波段间蕴含的非线性信息后,自适应地赋予特征空间RX算子中采样协方差矩阵各光谱向量相应的权值.权值的大小与光谱向量到质心的距离成反比,从而削减了协方差矩阵中异常数据比重,使加权协方差矩阵更好地表征背景数据分布.最后利用核函数性质将高维特征空间的内积运算转化为低维输入空间的核函数计算,并根据高光谱数据特点线性组合新型光谱核函数和径向基核函数以改善算法性能.为验证算法的有效性,利用真实的高光谱数据进行了仿真实验,结果表明该算法优于特征空间的RX算法,能检测到更多的异常目标.

英文摘要：

A new mixed kernel function weighted RX algorithm for anomaly detection in hyperspectral imagery was proposed.First,each spectral pixel was mapped into a high-dimensional feature space by a nonlinear mapping function.Second the nonlinear information between different spectral bands of the hyperspectral imagery was exploited with the RX algorithm in the feature space.In order to optimize the covariance matrix,each pixel in the covariance matrix was weighted according to its centroid distance.In this way the weighted covariance matrix could represent the background distribution better.Finally,the dot product computation in the high-dimensional feature space were converted into the kernel computation in the low dimensional input space.The new spectral kernel function and the radial basis kernel function were composited according to the characteristic of hyperspectral data to improve the performance of the proposed algorithm.To validate the effectiveness of the proposed algorithm,experiments were conducted on real hyperspectral data.The results show that the proposed method can detect more anomaly targets than the RX algorithm in the feature space.