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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.