像素到丰度转换：基于补丁 Transformer 的条件生成对抗网络用于高光谱解混
Pixel-to-Abundance Translation: Conditional Generative Adversarial Networks Based on Patch Transformer for Hyperspectral Unmixing
AI总结：
本文提出基于补丁 Transformer 的条件生成对抗网络 HyperGAN，将高光谱解混视为像素到丰度的模态转换，实现高效解混并取得优异实验结果。
摘要：
光谱解混是高光谱图像处理中的一个重大挑战。现有的解混方法利用关于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，作为一种通用的解混框架，其基于以下假设：从像素到丰度的解混过程可以被视为具有内部特定关系的两种模态之间的转换。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补丁到中心像素对应端元丰度的模态转换，后者用于区分生成的丰度的分布和结构是否与真实丰度相同。我们提出了高光谱图像（HSI）补丁 Transformer 作为生成器的主要组成部分，它利用自适应注意力分数来捕获 HSI 补丁内部像素的相关性，并以细粒度的方式利用空间-光谱信息来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，与最先进的竞争者相比，取得了令人印象深刻的结果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
