光谱端元选择是高光谱数据解混分析的重要前提。 在各种端元选择算法中, N-FINDR算法因其自动性和高效性受到广泛欢迎。 然而, 该算法需要进行数据降维预处理, 且包含大量的体积计算导致该算法的运算速度较慢, 限制了该算法的应用。 为此提出基于线性最小二乘支持向量机的N-FINDR改进算法, 该算法无需降维预处理, 且采用低复杂度的距离尺度代替复杂的体积尺度来加速算法。 此外还提出对野值点施加有效控制以赋予算法鲁棒性,以及利用像素预排序方法来降低算法的迭代次数。 实验结果表明, 基于线性最小二乘支持向量机的改进N-FINDR算法在保证选择效果的前提下复杂度大大降低, 鲁棒性方法和像素预排序方法则进一步提高了算法的选择效果和选择速度。Vector Machines

Endmember (EM) selection is an important prerequisite task for mixedspectral analysis of hyperspectral imagery. In all kinds of EM selection methods,N-FINDR has been a popular one for its full automation and efficient performance.Unfortunately, the implementation of the algorithm needs dimensional reduction inoriginal data, and the algorithm includes innumerable volume calculation. Thisleads to a low speed of the algorithm and so becomes a limitation to itsapplications. In the present paper, an improved N-FINDR algorithm was proposedbased on linear least square support vector machines (LLSSVM), which is free ofdimensional reduction and makes use of distance measure instead of volumeevaluation to speed up the algorithm. Additionally, it was also proposed to endowthe algorithm with robustness by controlling outliers. Experiments show that thecomputational load for EM selection using the improved N-FINDR algorithm based onLLSSVM was decreased greatly, and the selection effectiveness and the speed ofthe proposed algorithm were further improved by outlier removal and the pixelpre-sorting method respectively.support vector machines(LLSSVM); N-FINDR algorithm水下智能机器人技术国防科技重点实验室项目资助