现有的端元提取算法大多是基于凸面单形体假设，对于非单一地物类型，利用这些端元进行丰度反演将会影响混合像元分解精度。本文提出一种利用高分辨率图像判断高光谱像元内是否为同一类型地物的方法。首先，利用图像分割程序对高分辨率图像进行分割，得到光谱均一的斑块矢量图，并叠加到高光谱图像上；然后，通过空间关系分析找出斑块内的高光谱像元，称其为准端元；最后，利用端元提取算法在这些准端元中进行端元提取。实验结果表明，该方法将端元提取结果的误差降低了20%左右。

Existing endmember extraction algorithms are mainly based on the convex simplex hypothesis. However, the cover types in certain endmembers are not single, which will affect the unmixing accuracy of mixed pixels when performing abundance inversion. In this paper, we propose to determine the nature of the hyperspectral pixel based on the high-resolution remote sensing image. First, a spectral relatively homogeneous vector diagram of blocks is superimposed on the hyperspectral image after the high-resolution image segmentation. Second, spatial relations analysis is performed to find the hyperspectral pixels that are within the blocks, which is called a quasi-endmember. Finally, endmember extraction is performed to find endmembers from the quasi-endmember set. The experimental results demonstrate that our approach can reduce the root mean square error of the extraction results by about 20%.