Hyperspectral unmixing (HSU) is a way to process the prediction of the existing endmembers and the fractional abundances (FA) available in all pixels in the hyperspectral images. However, in a practical scenario, hyperspectral image is frequently corrupted due to many types of noises at the time of acquiring phenomenon such as dead-lines, impulse noise (IN), Gaussian noise (GN), and stripes. This type of complicated noise leads to mitigation in the quality of the acquired HSI, by making them to lose the precision process. To address these issues, this article presents a sturdy nonnegative matrix factorization (S-NMF) with integrated fast dissociable non-local Euclidean median and iterative block coordinate descent algorithm (IFD-NLEM-IBCDA), where FD-NLEM eliminates mixed noise from acquired HSI without degrading the original quality, then IB-CDA is utilized to unmix the HSI by solving S-NMF minimization problem. Furthermore, we also provided a solution for a hyper-parameter that is utilized in IB-CDA during the S-NMF minimization. Extensive simulation results, together on real and synthetic HSI, exhibit the superiority of anticipated unmixing under mixed noise conditions over conventional unmixing algorithms.

高光谱解混 (HSU) 是一种处理现有端元的预测和高光谱图像中所有像素可用的分数丰度 (FA) 的方法。然而，在实际场景中，高光谱图像经常由于获取时的多种噪声现象而被破坏，例如死线、脉冲噪声（IN）、高斯噪声（GN）和条纹。这种类型的复杂噪声会导致获取的 HSI 质量下降，因为它们会失去精确处理。为了解决这些问题，本文提出了一种稳固的非负矩阵分解 (S-NMF)，它具有集成的快速可分离非局部欧几里德中值和迭代块坐标下降算法 (IFD-NLEM-IBCDA)，其中 FD-NLEM 消除了采集到的混合噪声HSI 不降低原始质量，然后使用 IB-CDA 通过解决 S-NMF 最小化问题来解混 HSI。此外，我们还提供了在 S-NMF 最小化期间​​在 IB-CDA 中使用的超参数的解决方案。广泛的模拟结果，以及真实和合成的 HSI，展示了在混合噪声条件下预期解混优于传统解混算法的优势。