中文摘要：

传统高光谱异常探测器的背景统计信息易受异常目标干扰,鲁棒性较差,且难以探测非线性混合的异常目标.针对此问题,运用核特征投影理论,在异常探测器的背景信息构建中引入鲁棒性分析方法,提出了一种在核特征空间中具有鲁棒性的异常探测方法.该方法可以在不需要确定具体的非线性映射函数下,将高光谱数据从低维空间映射到高维特征空间,背景和目标在特征空间中可以用线性模型表示,并在特征空间中构造鲁棒性的探测器.该方法揭示了地物光谱间的高阶特性,可以较好地反映地物分布复杂的目标光谱特性.通过高光谱真实影像和模拟数据的实验证明：1）本文提出的异常探测方法具有更优的受试者工作特征曲线和曲线下面积统计值,目标和背景的分离度更大;2）在核特征空间内,排除异常目标对背景统计信息的干扰,有助于进一步提高探测准确度;3）特征提取可以更好地利用目标和背景的光谱区分性,是异常探测的重要步骤.

英文摘要：

Traditional hyperspectral anomaly detectors background statistics are easily contaminated by anomalies and not robust that is difficult to detect anomalies of nonlinear mixed.In response to these problems,the Kernel feature projection theory is utilized,robustness analysis method is introduced in the construction of the anomaly detector background information,and a robust anomaly detection method is proposed.Using this method,c hyperspectral data from low-dimensional space can be mapped to high dimensional feature space without specific nonlinear mapping function,the background and anomaly targets can be expressed by a linear model,and a robust detector is constructed in the feature space.This method reveals high-order features between the objects on ground surface and can reflect complex spectral characteristics of target and surface features distribution.Experiments of real hyperspectral images and simulated data can prove：1）the proposed anomaly detection method has a better receiver operating characteristic（ROC）curve and area under the curve（AUC） statistics and has a greater degree of separation of the target and background;2）in kernel feature space,exclusion of anomalies contamination on statistics of the background improve the detection accuracy;3）feature extraction can make better utilizing spectral diversity distinguish anomalies and background,which is an important step of anomaly detector.