In our previous work, a two-phase multiobjective sparse unmixing (Tp-MoSU) approach has been proposed, which settled the regularization parameter issues of the regularization unmixing methods. However, Tp-MoSU has limited performance in identifying the real endmembers from the highly noisy data in the first phase and cannot effectively exploit the spatial-contextual information in the second phase because of the similarity measure it used. To settle these two problems, a composite spectral similarity measure is first constructed by fusing the spectral correlation angle and the Euclidean distance. It is used instead of the Frobenius norm to measure the unmixing residuals in the first phase because it considers both the shape and amplitude discrepancy between two spectra simultaneously. Then, the $ {L_{2, \infty }}$ norm is used instead of the $ {l_2}$ norm to measure the unmixing residuals in the second phase, and the initialization, recombination, mutation, and local search strategies are also elaborately redesigned to help reduce this new objective, based on which the unmixing tasks of all pixels in a hyperspectral image can be completed at once. Therefore, this new measure facilitates the estimation of the abundances as a whole, and thus, the spatial-contextual information can be better exploited to improve the estimated abundances. Besides, the time efficiency for abundance estimation is also greatly improved. Experimental results demonstrate that the proposed method (termed as Tp-MoSU+) outperforms Tp-MoSU in both of the two phases under heavy noise and outperforms the tested regularization algorithms in estimating the abundances.

中文翻译：

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高光谱数据的高效两阶段多目标稀疏分解方法

在我们以前的工作中，提出了一种两阶段的多目标稀疏分解（Tp-MoSU）方法，解决了正则化混合方法的正则化参数问题。但是，Tp-MoSU在第一阶段从高噪声数据中识别真实端成员的性能有限，并且由于使用了相似性度量，因此在第二阶段无法有效利用空间上下文信息。为了解决这两个问题，首先通过融合光谱相关角和欧几里德距离来构造复合光谱相似性度量。因为它同时考虑了两个光谱之间的形状和幅度差异，所以它被用来代替Frobenius范数来测量第一相中的未混合残差。然后，$ {L_ {2，\ infty}} $ 使用规范代替 $ {l_2} $规范以测量第二阶段中的非混合残差，并且还精心设计了初始化，重组，突变和局部搜索策略，以帮助减少这一新目标，在此基础上可以完成高光谱图像中所有像素的混合任务立刻。因此，该新措施便于整体上估计丰度，因此可以更好地利用空间上下文信息来提高估计的丰度。此外，丰度估计的时间效率也大大提高。实验结果表明，所提出的方法（称为Tp-MoSU +）在重噪声下在两相方面均优于Tp-MoSU，并且在估计丰度方面也优于测试的正则化算法。