显著性权重RX高光谱异常点检测  

Saliency weighted RX hyperspectral imagery anomaly detection

作　　者：刘嘉诚 王爽[1] 刘伟华 胡炳樑[1] LIU Jiacheng;WANG Shuang;LIU Weihua;HU Bingliang(Key Laboratory of Spectral Imaging Technology,Xi’an Institute of Optical and Precision Machinery,Chinese Academy of Sciences,Xi’an 710119,China;University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]中国科学院西安光学精密机械研究所光谱成像技术重点实验室,西安710119 [2]中国科学院大学,北京100049

出　　处：《遥感学报》2019年第3期418-430,共13页NATIONAL REMOTE SENSING BULLETIN

基　　金：国家自然科学基金(编号:61501456,11327303,61405239);中国科学院西部青年学者项目基金(编号:XAB2016B20)~~

摘　　要：高光谱图像异常点检测中,传统RX异常点检测算法忽略了空间相关性,背景估计不准确。本文提出了一种基于图像局部邻域光谱显著性分析的加权RX算法。该算法通过引入图像显著性分析,对基于概率密度为权重的图像背景建模进行改进,建立光谱显著性权重图,重新定义RX算法中的均值向量和协方差矩阵,并给不同的目标赋予不同的权值,达到优化背景估计的目的。利用合成高光谱数据和真实高光谱数据进行异常点检测实验,结果表明,对于同一组数据,本文算法检测到的异常点数比传统算法多,虚警率较低,有效地提高了检测率。With the development of spectral imaging technique and its data processing technology, anomaly detection using hyperspectral data has become a popular topic. Anomaly detection refers to the search for sparse pixels of unknown spectral signals in hyperspectral imagery. Given that the anomaly detection is unsupervised, providing a priori information is necessary. Thus, anomaly detection has a strong practicality. Considering the lack of spatial correlation and low normal distribution adaptation, the traditional RX algorithm has an inaccurate background estimation. Thus, this algorithm is unsuitable for detecting hyperspectral data.In this study, a saliency weighted RX algorithm is proposed on the basis of the local neighborhood spectra of an image. When the human eye observes an image, the first object that is viewed is frequently the most significant. The significance of the saliency detection algorithm is to identify this goal. The saliency map is a 2 D image of the same size as the original image to represent the significance of the corresponding pixel in the original image. In this algorithm, the image background modeling based on probability density is improved by introducing a saliency analysis method. Afterward, the spectral saliency map is established, and the mean vector and covariance matrix of the RX algorithm are redefined. Saliency weighted RX algorithm provides different weights to optimize the background estimation.Anomaly detection experiments are conducted using synthetic and real hyperspectral data. Synthetic data experimental results show that, for each target, the number of anomalies detected using the saliency weighted RX algorithm is more than that of the traditional algorithms, and the saliency weighted RX algorithm can detect anomalies with abundance below 0.1. By contrast, traditional algorithms cannot detect these anomalies. Moreover, the false alarm pixels of the traditional algorithms are distributed in various positions, whereas the saliency weighted RX algorithm concentrates on an area

关 键 词：异常点检测 显著性 RX算法 高光谱图像处理 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]