基于目标正交子空间投影加权的高光谱图像异常检测算法

摘要

在高光谱图像的异常目标检测问题中,针对原始数据源不能正确地表征背景数据的分布而造成虚警概率较高的现象,本文提出了一种基于目标正交子空间投影加权的KRX高光谱图像异常检测算法。该算法从背景协方差矩阵的估算角度入手,将每个像素投影到目标的正交子空间中,为每个像素自适应地赋予合适的权值,从而减小目标信息的存在对背景特性估计的影响。并用AVIRIS高光谱数据进行了仿真实验,取得了较好的检测效果。将该算法与其他算法进行了比较,结果表明,本文提出算法的检测性能明显地优于传统算法,降低了虚警概率,具有较好的检测效果。%In the anomaly detection for hyperspectral image,the false rate is higher because that the original data sources can not correctly represent the distribution of the background data.To overcome this problem,a new weighted KRX algorithm based on target orthogonal subspace projection（OWKRX） is proposed.Starting with the estimation of background covariance matrix,the algorithm projects each pixel into the target orthogonal subspace,and self-adaptively gives each pixel a proper weight,thereby,diminishes the influence of background estimation because of the existence of target information.Numerical experiments were conducted on real hyperspectral images collected by AVIRIS.The results prove that the proposed algorithm outperforms existing algorithms,obtains better detection effect and lowers false rate.