An improved full automated endmember extraction algorithm based on endmember independence

Current algorithms of endmember extraction generally need to determine the number of endmembers manually. However, the number of endmembers is unknown in practical application, so an automated and iterative endmember extraction algorithm is put forward in this paper to solve the problem. Firstly, due to the spectral information of endmember is similar with its neighbors but noise is independent with others, we analyze the relevance between pixels and endmember in the concentric sliding window centered at each test endmember in order to eliminate the influence of noise. Then, due to the independence among endmembers, a candidate set formed of endmembers which have been extracted is constructed. We compute the correlation between the new endmember and the candidates in the set each time, if the largest correlation is small; the new one is added to the set. If the new one fails to join the set directly, we can take it to replace the existed in the set to increase the distance among endmembers. Finally, if the endmembers in the set remain unchanged in a few times, the iteration stops. The experiment shows that the improved algorithm have a near accuracy of endmember extraction with the traditional algorithm, meanwhile it weakens the influence of noise on the endmember extraction.