Real-Time Simplex Growing Algorithms for Hyperspectral Endmember Extraction

被引:54
作者
Chang, Chein-, I [1 ,2 ]
Wu, Chao-Cheng [3 ]
Lo, Chien-Shun [4 ]
Chang, Mann-Li [5 ,6 ,7 ]
机构
[1] Univ Maryland Baltimore Cty, Dept Comp Sci & Elect Engn, Remote Sensing Signal & Image Proc Lab, Baltimore, MD 21250 USA
[2] Natl Chung Hsing Univ, Dept Elect Engn, Taichung 40254, Taiwan
[3] Innovim, Greenbelt, MD 20770 USA
[4] Natl Formosa Univ, Dept Multimedia Design, Yunlin 632, Taiwan
[5] Kang Ning Jr Coll Med Care & Management, Dept Informat Management, Taipei 114, Taiwan
[6] Natl Open Univ, Dept Informat & Management, Taipei 247, Taiwan
[7] Bur Natl Hlth Insurance, Informat Syst Div, Taipei 10634, Taiwan
来源
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | 2010年 / 48卷 / 04期
关键词
Endmember extraction algorithm (EEA); p-Pass automatic target generation process (ATGP)-simplex growing algorithm (SGA); p-Pass Maximin-SGA; p-Pass Minimax-SGA; p-Pass real-time (RT) SGA (RT SGA); p-Pass unsupervised fully constrained least squares (UFCLS)-SGA; TARGET RECOGNITION; COMPONENT ANALYSIS; QUANTIFICATION; INFORMATION; SKEWERS; BLOCKS;
D O I
10.1109/TGRS.2009.2034979
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
The simplex growing algorithm (SGA) was recently developed as an alternative to the N-finder algorithm (N-FINDR) and shown to be a promising endmember extraction technique. This paper further extends the SGA to a versatile real-time (RT) processing algorithm, referred to as RT SGA, which can effectively address the following four major issues arising in the practical implementation for N-FINDR: 1) use of random initial endmembers which causes inconsistent final results; 2) high computational complexity which results from an exhaustive search for finding all endmembers simultaneously; 3) requirement of dimensionality reduction because of large data volumes; and 4) lack of RT capability. In addition to the aforementioned advantages, the proposed RT SGA can also be implemented by various criteria in endmember extraction other than the maximum simplex volume.
引用
收藏
页码:1834 / 1850
页数:17
相关论文
共 38 条
[1]   ICE: A statistical approach to identifying endmembers in hyperspectral images [J].
Berman, M ;
Kiiveri, H ;
Lagerstrom, R ;
Ernst, A ;
Dunne, R ;
Huntington, JF .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2004, 42 (10) :2085-2095
[2]  
BOARDMAN J. W., 1995, Summaries of JPL Airborne Earth Science Workshop, P23
[3]  
Bowles J., 2007, HYPERSPECTRAL DATA E, P77
[4]  
Chang C.-I., 2003, Hyperspectral Imaging: Techniques for Spectral Detection and Classification, DOI DOI 10.1007/978-1-4419-9170-6
[5]   A new growing method for simplex-based endmember extraction algorithm [J].
Chang, Chein-I ;
Wu, Chao-Cheng ;
Liu, Wei-min ;
Ouyang, Yen-Chieh .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2006, 44 (10) :2804-2819
[6]   An information-theoretic approach to spectral variability, similarity, and discrimination for hyperspectral image analysis [J].
Chang, CI .
IEEE TRANSACTIONS ON INFORMATION THEORY, 2000, 46 (05) :1927-1932
[7]   A fast iterative algorithm for implementation of pixel purity index [J].
Chang, CI ;
Plaza, A .
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2006, 3 (01) :63-67
[8]   Estimation of number of spectrally distinct signal sources in hyperspectral imagery [J].
Chang, CI ;
Du, Q .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2004, 42 (03) :608-619
[9]  
Chaudhry F., 2006, RECENT ADV HYPERSPEC
[10]  
CHU S, 2007, P SPIE C IM SPECTR 1