Current algorithms of endmember extraction basically need manually determining the number of endmembers

which is not conducive to automatically process. The paper puts forward iterative algorithm for automatic identification and extraction of endmember. First

we obtain the similarity threshold among pixels by statistical analysis

and determine the criterion of candidate endmembers. Then

the internal and external correlation judgments of candidate endmembers are done

and ill-conditioned matrix to circumvent judgment on endmember spectral set is conducted. Finally

the criterion of candidate endmembers is the end of the iterative conditions. When the hyperspectral image contains no candidate endmembers

the endmember spectral set is got and the numbers of endmembers are determined. Experiments show the effectiveness of this method

by which the error risk of sequential endmember extraction algorithm can be avoided

and the degree of automation is improved.