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中文摘要：
针对高光谱图像维数高且数据量大给数据分析和处理带来了极大的困难,提出了一种基于选择性分段主成分分析（SSPCA）算法的异常检测方法.该算法首先根据波段之间的相关性将一组多维的高光谱数据划分成多组波段子集,然后分别对各波段子集进行主成分分析,并综合每个波段子集中局部平均奇异度最大的一个波段,用于最后的KRX异常检测.最后用AVIRIS高光谱数据进行了实验研究,并与KRX算法以及选取信息量最大波段的相应算法进行了比较.结果表明,其检测性能得到了较好地改善,取得了较好的检测效果和较低的虚警率.
英文摘要：
Regarding the great difficulties created by the high dimensions and large volumes of a hyperspectral image,a new anomaly detection algorithm based on selective section principal component analysis（SSPCA） was introduced.First of all,the algorithm divided the spectral image of high dimensions into subset of low dimensions according to the correlation between spectral bands.Next,it performed PCA on every subset and selected one component with maximum singularity in every subset for KRX based on local average singularity（LAS）.Numerical experiments were conducted on real hyperspectral images collected by AVIRIS.The result proves that the proposed algorithm outperformed the other algorithms and obtained a better effect of detection and a lower false alarm rate.
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