基于局部核RX算法的高光谱实时检测
Received:April 25, 2016  Revised:September 29, 2016  点此下载全文
引用本文:赵春晖,姚淅峰.基于局部核RX算法的高光谱实时检测[J].Journal of Infrared and Millimeter Waves,2016,35(6):708~714
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Author NameAffiliationE-mail
ZHAO Chun-Hui Information and Communication Engineering College,Harbin Engineering University zhaochunhui@hrbeu.edu.cn 
YAO Xi-Feng Information and Communication Engineering College,Harbin Engineering University xf.yao1020@gmail.com 
基金项目:国家自然科学基金项目(面上项目,重点项目,重大项目);中国博士后科学基金
中文摘要:提出了一种基于LKRX检测器的实时异常检测算法.利用局部因果滑动阵列窗, 使检测系统保持因果性.根据卡尔曼滤波器的递归思想, 利用Hermitian矩阵分块求逆引理和Woodbury引理, 将LKRX算法中核协方差矩阵以及其逆矩阵以递归方式更新, 避免了数据的重复计算和逆矩阵的求解, 大大降低了算法复杂度.通过真实数据进行实验, 结果表明, 与LKRX算法相比, 实时LKRX算法在保持相同检测精度的同时, 消耗更少的计算时间; 而与实时RX算法相比, 实时LKRX算法能够检测到更多的异常目标.
中文关键词:高光谱图像处理  多项式KRX算法  实时异常检测  Hermitian矩阵分块求逆引理  Woodbury引理
 
Local kernel RX algorithm-based hyperspectral real-time detection
Abstract:LKRX detector-based hyperspectral real-time anomaly detection algorithm was proposed. Using local causal sliding array window, the causality of detection system is remained. According to Kalman filter, by using Hermitian lemma and Woodbury’s identity, the kernel covariance matrix and its inverse in KRX algorithm are updated recursively. This thereby leads to low computational complexity. Experimental results demonstrated that real-time KRX detector consumes less time in comparison with KRX detector by keeping the same detection performance, which detects more anomalies.
keywords:hyperspectral image processing, polynomial KRX algorithm, real-time anomaly detection, Hermitian lemma, Woodbury’s identity
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