Geng Xiurui, Sun Kang, Ji Luyan, Zhao Yongchao
Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Institute of Electronics, Chinese Academy of Sciences, Beijing, China.
Ministry of Education Key Laboratory for Earth System Modelling, Centre for Earth System Science, Tsinghua University, Beijing, China.
Sci Rep. 2014 Nov 4;4:6869. doi: 10.1038/srep06869.
Recently, high-order statistics have received more and more interest in the field of hyperspectral anomaly detection. However, most of the existing high-order statistics based anomaly detection methods require stepwise iterations since they are the direct applications of blind source separation. Moreover, these methods usually produce multiple detection maps rather than a single anomaly distribution image. In this study, we exploit the concept of coskewness tensor and propose a new anomaly detection method, which is called COSD (coskewness detector). COSD does not need iteration and can produce single detection map. The experiments based on both simulated and real hyperspectral data sets verify the effectiveness of our algorithm.
近年来，高阶统计量在高光谱异常检测领域受到越来越多的关注。然而，现有的大多数基于高阶统计量的异常检测方法都需要逐步迭代，因为它们是盲源分离的直接应用。此外，这些方法通常会产生多个检测图，而不是单个异常分布图像。在本研究中，我们利用共偏度张量的概念，提出了一种新的异常检测方法，称为COSD（共偏度检测器）。COSD不需要迭代，并且可以生成单个检测图。基于模拟和真实高光谱数据集的实验验证了我们算法的有效性。
