光谱解混是用于定量分析高光谱图像中成分含量的一项重要技术方法, 主要包括线性解混模型和非线性解混模型。 线性解混模型构造简单, 但并未考虑不同成分光子间的相互影响, 导致解混结果在很多实际图像上不够精确。 常用的非线性混合模型中的双线性解混算法, 随着图像中端元数量增加, 虚拟端元的数量也随之快速增加, 计算精度受到很大的影响。 论文报道改进了双线性解混的模型, 并提出一种有效端元子集的选择算法。 首先结合欧氏距离和光谱夹角, 按照与混合像元的距离, 将所有端元排序； 然后利用排序结果和误差变化情况选择实际参与混合的端元子集。 从而降低了未参与特定混合像元混合的端元对解混结果的影响, 提高了解混精度。 对模拟图像的测试效果证明了该算法可以减小光谱的重构误差, 对实际航拍高光谱溢油图像的分析结果也进一步说明了算法的有效性。

An effective endmembers based bilinear unmixing algorithm is prompted in the present paper together with an endmember subset selection algorithm as well. Firstly, the endmembers are ranked according to their distance to the mixed pixel, involving the Euclidean distance and spectral angle. And then, an effective subset of the endmembers is abstracted considering both the ranking result and the change of error. The algorithm reduces the influence of endmembers which are not component of the mixed pixel, decrease the number of endmembers involved in unmixing and improve the accuracy of abundance. The test results for simulation image prove that the algorithm would provide a lower reconstructing error. And the analysis results of actual airborne hyperspectral oil spill image further illustrate its effectiveness.