[1]成宝芝,赵春晖,张丽丽.子空间稀疏表示高光谱异常检测新算法[J].哈尔滨工程大学学报,2017,38(04):640-645.[doi:10.11990/jheu.201604006]
 CHENG Baozhi,ZHAO Chunhui,ZHANG Lili.An anomaly detection algorithm for hyperspectral images using subspace sparse representation[J].hebgcdxxb,2017,38(04):640-645.[doi:10.11990/jheu.201604006]
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子空间稀疏表示高光谱异常检测新算法(/HTML)
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《哈尔滨工程大学学报》[ISSN:1006-6977/CN:61-1281/TN]

卷:
38
期数:
2017年04期
页码:
640-645
栏目:
出版日期:
2017-04-25

文章信息/Info

Title:
An anomaly detection algorithm for hyperspectral images using subspace sparse representation
作者:
成宝芝12 赵春晖3 张丽丽23
1. 哈尔滨工程大学 计算机科学与技术学院, 黑龙江 哈尔滨 150001;
2. 大庆师范学院 机电工程学院, 黑龙江 大庆 163712;
3. 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 Baozhi12 ZHAO Chunhui3 ZHANG Lili23
1. College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China;
2. College of Physics and Electricity Information Engineering, Daqing Normal University, Daqing 163712, China;
3. College of Information and Communication, Harbin Engineering University, Harbin 150001, China
关键词:
高光谱图像异常目标检测子空间稀疏表示粒子群优化模糊聚类稀疏差异指数
分类号:
TP751.1
DOI:
10.11990/jheu.201604006
文献标志码:
A
摘要:
针对基于稀疏表示的高光谱异常目标检测新算法精度低的问题,提出了一种子空间稀疏表示的高光谱图像异常目标检测算法。该算法利用粒子群优化模糊C-均值聚类方法,在不改变高光谱图像光谱和空间特征的基础上,使得原始高光谱图像中具有相似特性的波段归为一类,从而将整个高光谱图像分为若干个波段子空间;利用光谱和空间协同加权稀疏差异指数公式对每一个子空间进行异常目标检测;对每个子空间的检测结果进行叠加,得到最终异常目标检测结果。利用真实的AVIRIS高光谱图像对算法进行仿真分析,结果表明该算法有较好的异常检测性能,检测精度高、虚警率低。

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备注/Memo

备注/Memo:
收稿日期:2016-4-2。
基金项目:国家自然科学基金项目(61571145);黑龙江省博士后基金项目(LBH-Z14062);大庆市指导性科技计划(ZD-2016-052);大庆师范学院博士基金项目(14ZR07)
作者简介:成宝芝(1976-),男,副教授;赵春晖(1965-),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: 2017-05-09