自动形态学端元提取(AMEE)算法中的形态学算子在纯像元集中分布的区域无法得到正确的结果。现有膨胀操作在每个结构元素内只能提取一个候选端元, 会造成重要像元丢失。为了解决这些问题, 采用改进的形态学算子和结构元素对AMEE算法进行了改进。首先引入参考光谱向量的概念构建了改进的形态学算子, 并给出了形态学离心率指数新的计算方法, 然后利用偶数大小、改进的结构元素, 从每个结构元素内选出4个候选端元, 最后对改进的基于自动形态学的端元提取算法进行了分析和实验验证。结果表明, 改进的方法能从纯像元集中分布的区域获得正确的候选端元, 并在一定程度上避免膨胀过程中的信息遗失, 从而能够有效地提升端元提取的精度和像元解混的效果。

Morphological operators used in the automated morphological endmember extraction (AMEE) algorithm didn’t acquire correct result in the area of pure pixel concentration distribution. The dilation operation only chose one candidate endmember from each structure element and would lose some important pixels. In order to solve the problem, the AMEE algorithm was modified by an improved morphological operator and new structural element. The improved morphological operator was proposed after introducing the concept of reference spectral vector, and a new calculation method of morphological eccentricity index was given. To avoid information loss, four candidate endmembers were chosen from each improved even-numbered structure element. The modified automated morphological endmember extraction algorithm was tested based on a hyperspectral data set. The experimental results show that the improved method can obtain correct candidate endmembers from the area of pure pixel concentration distribution, and information loss in the procedure of dilation is also avoided. The proposed method produces more accurate results of endmember extraction and the spectral unmixing.