Abstract. In consideration of within-class endmember variability, it is realistic to use multiple endmembers to model a pure class. We propose an advanced multi-endmember unmixing algorithm based on twin support vector machines (UTSVM), which derives the abundances based on the distances from the mixed pixels to each classification hyperplane. Unmixing uncertainty, an issue often neglected in multi-endmember unmixing, is also analyzed quantitatively for UTSVM. Two types of unmixing uncertainty, abundance overlap (i.e., different mixed pixels have the same abundances) and model overlap (i.e., one mixed pixel may be unmixed into different abundances), are introduced. Abundance overlap angle and abundance variability scale (AVS) are defined as two uncertainty indexes to measure abundance overlap and model overlap, respectively. The relationship between within-class endmember variability and unmixing uncertainty is discussed. When the unmixing uncertainty is high, we propose to use the mean value of abundances within AVS as the estimation of abundance to obtain the best compromised results. Experimental results show the feasibility and effectiveness of our study.

中文翻译：

---

基于双支持向量机的高光谱解混及其不确定性分析

摘要。考虑到类内端元的可变性，使用多个端元来建模一个纯类是现实的。我们提出了一种基于双支持向量机 (UTSVM) 的高级多端元解混合算法，该算法基于从混合像素到每个分类超平面的距离得出丰度。解混不确定性是多端元解混中经常被忽视的一个问题，也对 UTSVM 进行了定量分析。介绍了两种类型的解混不确定性，丰度重叠（即不同的混合像素具有相同的丰度）和模型重叠（即一个混合像素可能不混合成不同的丰度）。丰度重叠角和丰度变异尺度（AVS）被定义为两个不确定性指标，分别用于衡量丰度重叠和模型重叠。讨论了类内端元变异性和解混不确定性之间的关系。当解混不确定性很高时，我们建议使用 AVS 内丰度的平均值作为丰度的估计，以获得最佳的折衷结果。实验结果表明了我们研究的可行性和有效性。