[1]赵春晖,王佳,王玉磊.采用背景抑制和自适应阈值分割的高光谱异常目标检测[J].哈尔滨工程大学学报,2016,37(02):278-283.[doi:10.11990/jheu.201409035]
 ZHAO Chunhui,WANG Jia,WANG Yulei.Hyperspectral anomaly detection based on background suppression and adaptive threshold segmentation[J].hebgcdxxb,2016,37(02):278-283.[doi:10.11990/jheu.201409035]
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采用背景抑制和自适应阈值分割的高光谱异常目标检测(/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02期
页码:
278-283
栏目:
出版日期:
2016-02-25

文章信息/Info

Title:
Hyperspectral anomaly detection based on background suppression and adaptive threshold segmentation
作者:
赵春晖 王佳 王玉磊
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 Jia WANG Yulei
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
关键词:
高光谱图像背景抑制形态学滤波异常检测自适应阈值
分类号:
TP751.1
DOI:
10.11990/jheu.201409035
文献标志码:
A
摘要:
高光谱图像小目标异常检测存在着大面积背景异常的干扰,直接采用传统的阈值分割方法会产生较高的虚警。针对核RX异常检测算法存在较大面积背景干扰的现象,结合形态学滤波方法提取大面积背景杂波干扰并对其进行抑制,滤除背景干扰。然后采用自适应阈值方法对处理后的灰度值图像进行异常目标的分离。仿真实验结果表明,该方法较好地实现了对大面积背景干扰的抑制和对异常目标的保持,改善了现有的核RX算法用于高光谱异常检测的性能。

参考文献/References:

[1] 王立国, 赵春晖. 高光谱图像处理技术[M]. 北京: 国防工业出版社, 2013: 1-20. WANG Liguo, ZHAO Chunhui. Processing techniques of hyperspectral imagery[M]. Beijing: National Defence Industry Press, 2013: 1-20.
[2] 王玉磊, 赵春晖, 齐滨. 基于光谱相似度量的高光谱图像异常检测算法[J]. 吉林大学学报:工学版, 2013, 43(S1): 148-153. WANG Yulei, ZHAO Chunhui, QI Bin. Hyperspectral anomaly detection algorithm based on spectral similarity scale[J]. Journal of Jilin university:engineering and technology edition, 2013, 43(S1): 148-153.
[3] REED I S, YU Xiaoli. Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution[J]. IEEE transactions on acoustics, speech and signal processing, 1990, 38(10): 1760-1770.
[4] MANOLAKIS D G, SIRACUSA C, MARDEN D, et al. Hyperspectral adaptive matched filter detectors: practical performance comparison[C]//SPIE 4831, 2001: 18-33.
[5] HAZEL G G. Multivariate gaussian MRF for multispectral scene segmentation and anomaly detection[J]. IEEE transactions on geoscience and remote sensing, 2000, 38(3): 1199-1211.
[6] KWON H, NASRABADI N M. Kernel RX-algorithm: a nonlinear anomaly detector for hyperspectral imagery[J]. IEEE transactions on geoscience and remote sensing, 2005, 43(2): 388-397.
[7] ZHAO Chunhui, WANG Yulei, MEI Feng. Kernel ICA feature extraction for anomaly detection in hyperspectral imagery[J]. Chinese journal of electronics, 2012, 21(2): 265-269.
[8] 尤佳. 基于核方法的高光谱图像异常检测算法研究[D]. 哈尔滨: 哈尔滨工程大学, 2011: 48-51. YOU Jia. Hyperspectral image anomaly detection algorithm based on kernel method research[D]. Harbin: Harbin Engineering University, 2011: 48-51.

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备注/Memo

备注/Memo:
收稿日期:2014-9-15;改回日期:。
基金项目:国家自然科学基金资助项目(61571145,61405041);黑龙江省自然科学基金重点资助项目(ZD201216);哈尔滨市优秀学科带头人基金资助项目(RC2013XK009003).
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: 2016-03-15