无需混合模型先验，用生成对抗网络实现高光谱非线性解混。
Looking into a Pixel by Nonlinear Unmixing -- A Generative Approach
- 构建双向GAN框架，通过循环一致性和线性-非线性关联约束解混过程。
- 在多个数据集上表现稳定且优于现有主流方法。
- 适合对高光谱图像解混但缺乏物理模型的场景使用。
由于遥感图像中像素占据较大空间范围，高光谱解混（HU）已成为高光谱图像分析中的重要且必要步骤。传统解混方法依赖于先验光谱混合模型，尤其在处理非线性混合时，严重限制了方法的性能与泛化能力。本文针对无显式混合模型先验的高光谱非线性解混（HNU）难题，受生成模型原理启发——可在不明确图像概率分布函数的情况下生成同分布图像——提出一种基于双向GAN的可逆混合-解混流程，同时受循环一致性与线性/非线性混合间关联性的双重约束。该组合约束无需显式混合模型即可提供强大约束力。所提方法称为线性约束循环生成对抗网络解混网络（LCGU net）。实验结果表明，相比其他先进模型基解混方法，LCGU net在不同数据集上均展现出稳定且具有竞争力的性能。
原文摘要 · Abstract (English)
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.
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