In this study, we propose a novel framework for hyperspectral unmixing by using an improved deep spectral convolution network (DSCN++) combined with endmember uncertainty. DSCN++ is used to compute high-level representations which are further modeled with Multinomial Mixture Model to estimate abundance maps. In the reconstruction step, a new trainable uncertainty term based on a nonlinear neural network model is introduced to provide robustness to endmember uncertainty. For the optimization of the coefficients of the multinomial model and the uncertainty term, Wasserstein Generative Adversarial Network (WGAN) is exploited to improve stability. Experiments are performed on both real and synthetic datasets. The results validate that the proposed method obtains state-of-the-art hyperspectral unmixing performance particularly on the real datasets compared to the baseline techniques.
翻译:在本研究中,我们提出了一个超光谱混合的新框架,即使用经改进的深光谱相联网络(DSCN+++),加上终端成员不确定性。DSCN+用来计算高级代表机构,这些代表机构与多元混合模型进一步建模,以估算丰度图。在重建阶段,根据非线性神经网络模型引入一个新的可训练的不确定性术语,以便为最终成员不确定性提供稳健性。为了优化多光谱模型的系数和不确定性术语,利用了瓦塞尔斯坦·格恩纳蒂·德versarial网络(WGAN)来提高稳定性。在真实和合成数据集上都进行了实验。结果验证,认为拟议的方法获得了最新超光谱分解性功能,特别是相对于基线技术而言,真实数据集的超光谱分解性能。