Due to the large footprint of pixels in remote sensing imagery, hyperspectral unmixing (HU) has become an important and necessary procedure in hyperspectral image analysis. Traditional HU methods rely on a prior spectral mixing model, especially for nonlinear mixtures, which has largely limited the performance and generalization capacity of the unmixing approach. In this paper, we address the challenging problem of hyperspectral nonlinear unmixing (HNU) without explicit knowledge of the mixing model. Inspired by the principle of generative models, where images of the same distribution can be generated as that of the training images without knowing the exact probability distribution function of the image, we develop an invertible mixing-unmixing process via a bi-directional GAN framework, constrained by both the cycle consistency and the linkage between linear and nonlinear mixtures. The combination of cycle consistency and linear linkage provides powerful constraints without requiring an explicit mixing model. We refer to the proposed approach as the linearly-constrained CycleGAN unmixing net, or LCGU net. Experimental results indicate that the proposed LCGU net exhibits stable and competitive performance across different datasets compared with other state-of-the-art model-based HNU methods.

翻译：由于遥感影像中像素的覆盖范围较大，高光谱解混已成为高光谱图像分析中一项重要且必要的步骤。传统高光谱解混方法依赖先验光谱混合模型（尤其在非线性混合场景下），这很大程度上限制了解混方法的性能与泛化能力。本文针对无显式混合模型知识的非线性高光谱解混这一挑战性问题，受生成模型原理启发（该原理下无需知晓图像精确概率分布函数即可生成与训练图像同分布的图像），通过双向生成对抗网络框架构建可逆的混合-解混过程，并采用循环一致性约束及线性与非线性混合间的关联约束。循环一致性与线性关联的结合提供了无需显式混合模型的强约束条件。我们将所提方法称为线性约束CycleGAN解混网络。实验结果表明，与当前其他基于模型的最优非线性高光谱解混方法相比，所提网络在不同数据集上均展现出稳定且具有竞争力的性能。