Target Detection Algorithm in Hyperspectral Imagery Based on FastICA

被引：3
|
作者：
Zheng Mao [1 ]
Zan Decai [2 ]
Zhang Wenxi [3 ]
机构：
[1] Natl Univ Def Technol, Sch Elect Sci & Engn, Changsha 410073, Hunan, Peoples R China
[2] Hebet Engn & Tech Coll, Dept Comp Network, Cangzhou 061001, Peoples R China
[3] Changsha Univ, Dept Elect Commun Engn, Changsha 410003, Peoples R China
来源：
2ND IEEE INTERNATIONAL CONFERENCE ON ADVANCED COMPUTER CONTROL (ICACC 2010), VOL. 5 | 2010年
关键词：
Independent Component Analysis; Noise-Adjusted Principal Component Analysis; Unsupervised Orthogonal Subspace Projection; Hyperspectral Imagery; Endmember extraction; CLASSIFICATION;
D O I：
10.1109/ICACC.2010.5487134
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The target detection algorithm based on Independent Component Analysis (ICA) was proposed. The orthogonal subspace projection operator was used to extract the target endmembers and the initialization mixing matrix of the Fast_ICA was made up of such endmember vectors. This method could solve the ordering randomicity of independent vectors. In this paper, the Noise-Adjusted Principal Component Analysis (NAPCA) was used to reduce the dimensionality of the original data to reduce the calculation. The ICA transformation of the reserved principal components was developed to detect the targets. The experimental results based on AVIRIS hyperspectral imagery have shown that it is more effective than the CEM method.
引用
收藏
页码：579 / 582
页数：4
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