中文摘要：

为了解决传统N-FINDR算法计算量大,提取结果对噪声和初始端元选取敏感,且容易将异常点作为端元而造成误提取的问题,提出一种改进的快速N-FINDR端元提取算法.该方法通过光谱距离提取并去除高光谱图像中的冗余信息,减少N-FINDR提取端元的搜索范围,平滑噪声影响,并自适应剔除异常点,通过最大化光谱距离选取N-FINDR的初始端元,避免了随机选择的盲目性.采用合成数据和真实高光谱数据进行仿真分析并与现有算法进行对比,结果表明,本文算法能在噪声与奇异点干扰下正确提取端元,其提取效率和鲁棒性均优于现有算法.

英文摘要：

Traditional N-FINDR algorithm suffers from complicated calculations,and is sensitive to noise and initial endmembers,resulting in wrong extraction by outliers.In order to solve these problems,an improved fast N-FINDR endmember extraction algorithm was proposed,which gets rid of the redundant information and reduces the search area of commonly used N-FINDR algorithm by spectral distance,then smoothes the spectral noise and gets rid of the outliers adaptively.In addition,the initial endmembers of N-FINDR are selected by maximizing the spectral distance,avoiding the blindness of random selection.The synthetic data and real hyperspectral data were used for simulation analysis,and the proposed method was compared with existing algorithms.The experiment results show that the proposed method is able to extract the endmembers correctly in the noise,and has higher extraction efficiency and robustness than existing algorithms.