Please use this identifier to cite or link to this item:
標題: Real-Time Simplex Growing Algorithms for Hyperspectral Endmember Extraction
作者: Chang, C.I.
Wu, C.C.
Lo, C.S.
Chang, M.L.
關鍵字: 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;pixel purity index;target recognition;component analysis;image-analysis;quantification;information;skewers;blocks
Project: Ieee Transactions on Geoscience and Remote Sensing
期刊/報告no:: Ieee Transactions on Geoscience and Remote Sensing, Volume 48, Issue 4, Page(s) 1834-1850.
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.
ISSN: 0196-2892
DOI: 10.1109/tgrs.2009.2034979
Appears in Collections:期刊論文

Show full item record

Google ScholarTM




Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.