异常目标检测是高光谱数据处理的重要应用之一. 传统方法采用支持向量数据描述(support vector datadescription, SVDD)检测异常目标而不考虑图像自身存在的背景干扰，检测概率较低. 该文提出一种新方法，将光谱解混技术引入到基于SVDD的异常检测问题中，实现高光谱图像复杂背景信息和目标信息的分离，使解混后的误差数据含有丰富的目标信息，抑制了背景干扰. 利用非线性SVDD将解混误差数据映射到高维特征空间，充分利用高光谱图像波段间的非线性统计特性，完成异常目标的检测. 仿真实验结果表明，该算法提高了异常目标的检测能力，降低了虚警率.

 Without considering background interferences, the classical algorithm of support vector data description (SVDD) has relatively low detection probability in hyperspectral anomaly detection. To solve the problem, this paper presented a new algorithm based SVDD, which includes hyperspectral unmixing to separate
target information from complicated background clutter. After spectral unmixing, the error datum includes abundant target information while effectively suppresses the background interference. The error datum is then mapped into a high-dimensional feature space with nonlinear SVDD. By exploiting nonlinear information between the spectral bands of hyperspectral imagery, anomaly targets are detected. The results show that the proposed algorithm can improve detection performance and decrease false alarm probability.