针对Isomap-NFINDR端元提取算法复杂度高、占用内存多、效率低的缺点, 提出一种基于标志点选择Isomap的快速端元提取算法。该方法采用最大最小距离法来选取初始的K个聚类中心点, 并采用光谱夹角距离SAD代替欧式距离来进行聚类分割; 根据图像的空间特性, 从去除聚类的边界点后剩余点间隔抽取距离聚类中心距离最小的N个点作为标志点。真实高光谱图像实验结果表明, 提出的算法精度接近原始的基于Isomap-NFINDR算法, 而效率提高了将近60倍。

A fast endmember extraction method based on landmark point selection is presented to overcome the high complexity and memory usage of the classical Isomap-NFINDR algorithm. The proposed method uses the maximin distance method to initial the k cluster centers, and carries out clustering segmentation using spectral angle instead of Euclidean distance. According to the spatial characteristics of the image, N landmark points which are near to cluster center are selected from the remaining points after removing the boundary points. Experiments with real images reveal that the algorithm proposed has the similar accuracy with the original algorithm and its operational efficiency is improved by 60 times.