Fast N-FINDR algorithm for endmember extraction based on chi-square distribution
利用卡方分布改进N-FINDR端元提取算法

N-FINDR algorithm were employed for endmember extraction for decomposing the mixed pixels, which searches for each pixel from the dimension reduced feature space induced using principal component transformation or maximum noise factor transformation method. Due to the large search range for the endmembers, the efficiency of the N-FINDR algorithm is low. In this paper, we proposed the improved fast N-FINDR algorithm aiming to decrease the computation cost by providing a relative smaller search range, i.e. the candidate endmember set which was only a subset of the entire feature space. N-FINDR algorithm assumed that all the endmembers located at the vertexes of the simplex, which means that these pixels should be far away from the central part of all the pixels. Therefore, the percentile of chi-square distribution can be used to segment out these possible endmembers into a candidate set, which has much smaller size. The performance of the proposed algorithm has been verified using both synthetic and real hyperspectral data. Under the same endmember extraction precision, the modified N-FINDR algorithm has faster computation velocity and a higher overall efficiency.