Title :
A New Maximum Simplex Volume Method Based on Householder Transformation for Endmember Extraction
Author :
Liu, Junmin ; Zhang, Jiangshe
Author_Institution :
State Key Lab. for Manuf. Syst. Eng., Xi´´an Jiaotong Univ., Xi´´an, China
Abstract :
Endmember extraction is very important in hyperspectral image analysis. The accurate identification of endmembers enables target detection and classification and efficient spectral unmixing. Although a number of endmember extraction algorithms have been proposed, such as two state-of-the-art algorithms-vertex component analysis (VCA) and simplex growing algorithm (SGA)-it is still a rather challenging task. In this paper, a new maximum simplex volume method based on Householder transformation (HT), referred to as maximum volume by HT (MVHT), is presented for endmember extraction. The proposed algorithm provides consistent results with low computational complexity, which overcomes the disadvantage of the inconsistent result of VCA and the shortcoming of the high computational cost of SGA resulted from calculating the simplex volume. A comparative study and analysis are conducted among the three endmember extraction algorithms, VCA, SGA, and MVHT, on both simulated and real hyperspectral data. The obtained experimental results demonstrate that the proposed MVHT algorithm generally provides a competitive or even better performance over VCA and SGA.
Keywords :
geophysical image processing; remote sensing; MVHT algorithm; computational complexity; endmember extraction algorithm; householder transformation; hyperspectral data; hyperspectral image analysis; maximum simplex volume method; remote sensing image processing; simplex growing algorithm; spectral unmixing; state-of-the-art algorithms; target detection; vertex component analysis; Algorithm design and analysis; Approximation algorithms; Approximation methods; Computational complexity; Hyperspectral imaging; Indexes; Pixel; Endmember extraction; maximum simplex volume; simplex growing algorithm (SGA); vertex component analysis (VCA);
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
DOI :
10.1109/TGRS.2011.2158829