Modified Kernel RX Algorithm Based on Background Purification and Inverse-of-Matrix-Free Calculation

Chunhui Zhao; Xi-Feng Yao; Yiming Yan

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Modified Kernel RX Algorithm Based on Background Purification and Inverse-of-Matrix-Free Calculation

机译：基于背景净化和无矩阵逆计算的改进核RX算法

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摘要

The kernel RX detector (KRXD) has better performance than the RX algorithm in anomaly detection (AD). However, it generally suffers from two challenges: 1) it is more prone to background contamination by anomalous pixels and noise in local statistics since the local AD is normally implemented for KRXD to relieve high computational complexity in global AD and 2) the inverse of the kernelized background covariance matrix is usually rank deficient. Accordingly, this letter proposes a Gaussian background purification approach according to background data samples probability distribution and an inverse-of-matrix-free method based on kernel PCA to address the above problems, respectively. The experimental results indicate that the improved KRXD overcomes both the difficulties and procures preferable effects.

机译：内核RX检测器（KRXD）在异常检测（AD）中具有比RX算法更好的性能。但是，它通常会面临两个挑战：1）由于通常为KRXD实施本地AD以减轻全局AD中的高计算复杂度，因此本地统计中的像素异常和噪声更容易造成背景污染； 2）反之。核化背景协方差矩阵通常秩不足。因此，本文分别根据背景数据样本的概率分布和基于核PCA的无矩阵逆方法提出了一种高斯背景净化方法，以解决上述问题。实验结果表明，改进的KRXD克服了困难，取得了较好的效果。

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来源
《IEEE Geoscience and Remote Sensing Letters》 |2017年第4期|544-548|共5页
作者
Chunhui Zhao; Xi-Feng Yao; Yiming Yan;
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作者单位

College of Information and Telecommunications, Harbin Engineering University, Harbin, China;

College of Information and Telecommunications, Harbin Engineering University, Harbin, China;

College of Information and Telecommunications, Harbin Engineering University, Harbin, China;

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正文语种 eng
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关键词
Covariance matrices; Hyperspectral imaging; Kernel; Eigenvalues and eigenfunctions; Matrix decomposition; Gaussian distribution;

机译：协方差矩阵;高光谱成像;核;特征值和特征函数;矩阵分解;高斯分布;

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Modified Kernel RX Algorithm Based on Background Purification and Inverse-of-Matrix-Free Calculation

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