Pixel-to-Abundance Translation: Conditional Generative Adversarial
  Networks Based on Patch Transformer for Hyperspectral Unmixing
Li Wang Xiaohua Zhang Longfei Li Hongyun Meng Xianghai Cao
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.