Spectral unmixing is a significant challenge in hyperspectral image processing. Existing unmixing methods utilize prior knowledge about the abundance distribution to solve the regularization optimization problem, where the difficulty lies in choosing appropriate prior knowledge and solving the complex regularization optimization problem. To solve these problems, we propose a hyperspectral conditional generative adversarial network (HyperGAN) method as a generic unmixing framework, based on the following assumption: the unmixing process from pixel to abundance can be regarded as a transformation of two modalities with an internal specific relationship. The proposed HyperGAN is composed of a generator and discriminator, the former completes the modal conversion from mixed hyperspectral pixel patch to the abundance of corresponding endmember of the central pixel and the latter is used to distinguish whether the distribution and structure of generated abundance are the same as the true ones. We propose hyperspectral image (HSI) Patch Transformer as the main component of the generator, which utilize adaptive attention score to capture the internal pixels correlation of the HSI patch and leverage the spatial-spectral information in a fine-grained way to achieve optimization of the unmixing process. Experiments on synthetic data and real hyperspectral data achieve impressive results compared to state-of-the-art competitors.

翻译：光谱解混是高光谱图像处理中的一个重要挑战。现有解混方法利用丰度分布的先验知识来解决正则化优化问题，其难点在于选择合适的先验知识并求解复杂的正则化优化问题。为解决这些问题，我们提出了一种高光谱条件生成对抗网络（HyperGAN）方法作为通用解混框架，其基于以下假设：从像素到丰度的解混过程可视为具有内在一一对应关系的两种模态间的转换。所提出的HyperGAN由生成器和判别器组成，前者完成从混合高光谱像素块到中心像素对应端元丰度的模态转换，后者用于判别生成的丰度分布与结构是否与真实丰度相同。我们提出高光谱图像补丁Transformer作为生成器的主要组件，该模块利用自适应注意力分数捕捉高光谱补丁内部像素相关性，并以细粒度方式利用空间-光谱信息实现解混过程的优化。在合成数据和真实高光谱数据上的实验相比现有最优方法取得了令人瞩目的结果。