Hyperspectral unmixing is of vital importance within the realm of hyperspectral analysis, which is aimed to decide the fractional proportion (abundances) of fundamental spectral signatures (endmembers) at a subpixel level. Unsupervised unmixing techniques that employ autoencoder (AE) network have gained significant attention for its exceptional feature extraction capabilities. However, traditional AE-based methods lean towards focusing excessively on the information of spectral dimension in the data, resulting in limited ability to extract endmembers with meaningful physical interpretations, and achieve uncompetitive performance. In this paper, we propose a novel multi-domain dual-stream network, called MdsNet, which enhances performance by incorporating high-rank spatial information to guide the unmixing process. This approach allows us to uncover pure endmember data that is hidden within the original hyperspectral image (HSI). We first apply superpixel segmentation and smoothing operations as preprocessing steps to transform the HSI into a coarse domain. Then, MdsNet efficiently handles multi-domain data and employs attention generated from the approximate domain to learn meaningful information about the endmembers’ physical characteristic. Experimental results and ablation studies conducted on Synthetic and real datasets (Samson, Japser, Urban) outperform state-of-the-art techniques by more than 10% in terms of root mean squared error and spectral angle distance, illustrating the effectiveness and superiority of our proposed method. The source code is available at https://github.com/qiwenjjin/JAG-MdsNet.

中文翻译：

---

用于高光谱解混的多域双流网络


高光谱解混在高光谱分析领域中至关重要，高光谱分析旨在确定亚像素级别基本光谱特征（端元）的分数比例（丰度）。采用自动编码器 （AE） 网络的无监督解混技术因其卓越的特征提取能力而受到广泛关注。然而，传统的基于 AE 的方法倾向于过度关注数据中的光谱维度信息，导致提取具有有意义的物理解释的端元的能力有限，并实现无竞争力的性能。在本文中，我们提出了一种新颖的多域双流网络，称为 MdsNet，它通过结合高级空间信息来指导解混过程来提高性能。这种方法使我们能够发现隐藏在原始高光谱图像 （HSI） 中的纯终元数据。我们首先应用超像素分割和平滑操作作为预处理步骤，将 HSI 转换为粗略域。然后，MdsNet 有效地处理多域数据，并利用从近似域产生的注意力来了解有关末端成员物理特征的有意义信息。在合成和真实数据集 （Samson， Japser， Urban） 上进行的实验结果和消融研究在均方根误差和光谱角距离方面比最先进的技术高出 10% 以上，说明了我们提出的方法的有效性和优越性。源代码可在 https://github.com/qiwenjjin/JAG-MdsNet 获取。