论文标题
与多项式混合物内核和Wasserstein生成对抗性损失的光谱脉络
Spectral Unmixing With Multinomial Mixture Kernel and Wasserstein Generative Adversarial Loss
论文作者
论文摘要
这项研究提出了一个新型的框架，用于使用1D卷积内核和光谱不确定性，以实现光谱脉络。从数据计算出高级表示，并通过多项式混合模型进一步对其进行建模，以在严重的光谱不确定性下估计分数。此外，在重建步骤中介绍了基于非线性神经网络模型的新的可训练不确定性项。 Wasserstein生成对抗网络（WGAN）优化了所有不确定性模型，以提高稳定性并捕获不确定性。实验是对真实和合成数据集进行的。结果验证了所提出的方法获得了最先进的性能，尤其是对于基准相比，对于实际数据集而言。项目页面：https：//github.com/savasozkan/dscn。
This study proposes a novel framework for spectral unmixing by using 1D convolution kernels and spectral uncertainty. High-level representations are computed from data, and they are further modeled with the Multinomial Mixture Model to estimate fractions under severe spectral uncertainty. Furthermore, a new trainable uncertainty term based on a nonlinear neural network model is introduced in the reconstruction step. All uncertainty models are optimized by Wasserstein Generative Adversarial Network (WGAN) to improve stability and capture uncertainty. Experiments are performed on both real and synthetic datasets. The results validate that the proposed method obtains state-of-the-art performance, especially for the real datasets compared to the baselines. Project page at: https://github.com/savasozkan/dscn.
