[1]成宝芝,赵春晖.联合空间预处理与双边滤波的稀疏RX高光谱异常检测[J].哈尔滨工程大学学报,2019,40(04):851-857.[doi:10.11990/jheu.201802031]
 CHENG Baozhi,ZHAO Chunhui.Joint spatial preprocessing and bilateral filtering of sparsity RX anomaly detection for hyperspectral imagery[J].hebgcdxxb,2019,40(04):851-857.[doi:10.11990/jheu.201802031]
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《哈尔滨工程大学学报》[ISSN:1006-6977/CN:61-1281/TN]

卷:
40
期数:
2019年04期
页码:
851-857
栏目:
出版日期:
2019-04-05

文章信息/Info

Title:
Joint spatial preprocessing and bilateral filtering of sparsity RX anomaly detection for hyperspectral imagery
作者:
成宝芝1 赵春晖2
1. 大庆师范学院 机电工程学院, 黑龙江 大庆 163712;
2. 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):
CHENG Baozhi1 ZHAO Chunhui2
1. College of Mechanical and Electrical Engineering, Daqing Normal University, Daqing 163712, China;
2. College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
关键词:
高光谱遥感图像异常检测稀疏表示空间预处理双边滤波背景数据高斯滤波核函数
分类号:
TP751.1
DOI:
10.11990/jheu.201802031
文献标志码:
A
摘要:
针对RX异常目标检测算法对高光谱图像异常目标检测精度低和虚警率高的问题,本文提出一种充分利用高光谱图像的空间信息和光谱信息,并联合高光谱图像自身存在的稀疏特性,对经典RX异常检测算法进行改进,得到一种稀疏RX异常目标检测算法。通过利用空间预处理方法抑制背景数据信息,使异常目标点突出,然后利用双边滤波方法再次对高光谱图像进行滤波处理,滤除噪声干扰对高光谱图像的影响;在此基础上,利用稀疏表示理论,计算高光谱图像的稀疏差异指数,再利用稀疏差异指数重构一个高光谱图像数据向量,最后利用RX方法进行异常目标检测,得到异常目标检测结果。利用高光谱图像进行仿真验证,能够得到算法的检测精度高、虚警率低和鲁棒性好。

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备注/Memo

备注/Memo:
收稿日期:2018-02-27。
基金项目:国家自然科学基金项目(61571145);教育部产教联合基金项目(2017B00001).
作者简介:成宝芝,男,副教授,博士后;赵春晖,男,教授,博士生导师.
通讯作者:成宝芝,E_mail:chengbaozhigy@163.com
更新日期/Last Update: 2019-04-03