[1]赵春晖,王鑫鹏,闫奕名.基于密度背景纯化的高光谱异常检测算法[J].哈尔滨工程大学学报,2016,37(12):1722-1727.[doi:10.11990/jheu.201511073]
 ZHAO Chunhui,WANG Xinpeng,YAN Yiming.Density background refinement-based anomaly detection algorithm for hyperspectral images[J].hebgcdxxb,2016,37(12):1722-1727.[doi:10.11990/jheu.201511073]
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基于密度背景纯化的高光谱异常检测算法(/HTML)
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《哈尔滨工程大学学报》[ISSN:1006-6977/CN:61-1281/TN]

卷:
37
期数:
2016年12期
页码:
1722-1727
栏目:
出版日期:
2016-12-25

文章信息/Info

Title:
Density background refinement-based anomaly detection algorithm for hyperspectral images
作者:
赵春晖 王鑫鹏 闫奕名
哈尔滨工程大学 信息与通信工程学院, 黑龙江 哈尔滨 150001
Author(s):
ZHAO Chunhui WANG Xinpeng YAN Yiming
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
关键词:
高光谱图像遥感异常检测密度纯化RX检测算法最大类间方差法接收机工作特性
分类号:
TP751.1
DOI:
10.11990/jheu.201511073
文献标志码:
A
摘要:
在高光谱图像异常检测中,背景存在异常像元会造成背景统计信息失真,这将导致检测结果具有较高的虚警率。针对此问题,本文提出了一种基于密度背景纯化的异常检测算法。首先计算背景中每个像元的密度;然后根据高光谱图像中背景密度远大于异常密度的特性,利用最大类间方差法将异常从背景中分离;最后,将纯化后的背景用于统计信息的估计,通过RX检测算法(Reed-Xiaoli detector,RXD)对高光谱图像进行检测。为验证算法的有效性,利用两组真实的高光谱数据进行仿真实验。实验结果表明与RXD比,所提算法在两组数据下的曲线下面积值分别提高了0.024 6和0.008 6。与当前的异常检测算法相比:所提算法有较好的接收机工作特性曲线。

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备注/Memo

备注/Memo:
收稿日期:2015-11-28
基金项目:国家自然科学基金项目(61571145,61601135);黑龙江省自然科学基金项目(ZD201216);哈尔滨市优秀学科带头人基金项目(RC2013XK009003);中央高校基本科研业务费专项基金项目(GK2080260139).
作者简介:赵春晖(1965-),男,教授,博士生导师.
通讯作者:赵春晖,E-mail:zhaochunhui@hrbeu.edu.cn.
更新日期/Last Update: 2017-01-06