[1]赵春晖,胡春梅,石红.采用选择性分段PCA算法的高光谱图像异常检测[J].哈尔滨工程大学学报,2011,(01):109-113.[doi:doi:10.3969/j.issn.1006-7043.2011.01.020]
 ZHAO Chunhui,HU Chunmei,SHI Hong.Anomaly detection for a hyperspectral image by using a selective section principal component analysis algorithm[J].hebgcdxxb,2011,(01):109-113.[doi:doi:10.3969/j.issn.1006-7043.2011.01.020]
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采用选择性分段PCA算法的高光谱图像异常检测(/HTML)
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《哈尔滨工程大学学报》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2011年01期
页码:
109-113
栏目:
出版日期:
2011-01-25

文章信息/Info

Title:
Anomaly detection for a hyperspectral image by using a selective section principal component analysis algorithm
文章编号:
1006-7043(2010)11-0109-05
作者:
赵春晖 胡春梅石红
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; HU Chunmei; SHI Hong
College of Information and Communication Engineering; Harbin Engineering University; Harbin 150001; China
关键词:
高光谱图像选择性分段主成分分析局部平均奇异度KRX异常检测
分类号:
TN911
DOI:
doi:10.3969/j.issn.1006-7043.2011.01.020
文献标志码:
A
摘要:
针对高光谱图像维数高且数据量大给数据分析和处理带来了极大的困难,提出了一种基于选择性分段主成分分析(SSPCA)算法的异常检测方法.该算法首先根据波段之间的相关性将一组多维的高光谱数据划分成多组波段子集,然后分别对各波段子集进行主成分分析,并综合每个波段子集中局部平均奇异度最大的一个波段,用于最后的KRX异常检测.最后用AVIRIS高光谱数据进行了实验研究,并与KRX算法以及选取信息量最大波段的相应算法进行了比较.结果表明,其检测性能得到了较好地改善,取得了较好的检测效果和较低的虚警率.

参考文献/References:

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[11]赵春晖,胡春梅,石红.采用选择性分段PCA算法的高光谱图像异常检测[J].哈尔滨工程大学学报,2010,(11):0.
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备注/Memo

备注/Memo:
国家自然科学基金资助项目(61077079,60802059);博士点基金资助项目(20102304110013);哈尔滨市优秀学科带头人基金资助项目(2009RFXXG034)
更新日期/Last Update: 2011-03-30