Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/44538
標題: A new growing method for simplex-based endmember extraction algorithm
作者: Chang, C.I.
歐陽彥杰
Wu, C.C.
Liu, W.M.
Ouyang, Y.C.
關鍵字: endmember extraction
N-finder algorithm (N-FINDR)
pixel purity index
(PPI)
sequential endmember extraction algorithm (SQEEA)
simplex
growing algorithm (SGA)
simultaneous endmember extraction algorithm
(SMEEA)
vertex component analysis (VCA)
virtual dimensionality (VD)
hyperspectral image-analysis
dimensionality reduction
component
analysis
期刊/報告no:: Ieee Transactions on Geoscience and Remote Sensing, Volume 44, Issue 10, Page(s) 2804-2819.
摘要: A new growing method for simplex-based endmemher extraction algorithms (EEAs), called simplex growing algorithm (SGA), is presented in this paper. It is a sequential algorithm to find a simplex with the maximum volume every time a new vertex is added. In order to terminate this algorithm a recently developed concept, virtual dimensionality (VD), is implemented as a stopping rule to determine the number of vertices required for the algorithm to generate. The SGA improves one commonly used EEA, the N-finder algorithm (N-FINDR) developed by Winter, by including a process of growing simplexes one vertex at a time until it reaches a desired number of vertices estimated by the VD, which results in a tremendous reduction of computational complexity. Additionally, it also judiciously selects an appropriate initial vector to avoid a dilemma caused by the use of random vectors as its initial condition in the N-FINDR where the N-FINDR generally produces different sets of final endmembers if different sets of randomly generated initial endmembers are used. In order to demonstrate the performance of the proposed SGA, the N-FINDR and two other EEAs, pixel purity index, and vertex component analysis are used for comparison.
URI: http://hdl.handle.net/11455/44538
ISSN: 0196-2892
文章連結: http://dx.doi.org/10.1109/tgrs.2006.881803
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