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In this study, we propose a novel framework for hyperspectral unmixing byusing an improved deep spectral convolution network (DSCN++) combined withendmember uncertainty. DSCN++ is used to compute high-level representationswhich are further modeled with Multinomial Mixture Model to estimate abundancemaps. In the reconstruction step, a new trainable uncertainty term based on anonlinear neural network model is introduced to provide robustness to endmemberuncertainty. For the optimization of the coefficients of the multinomial modeland the uncertainty term, Wasserstein Generative Adversarial Network (WGAN) isexploited to improve stability and to capture uncertainty. Experiments areperformed on both real and synthetic datasets. The results validate that theproposed method obtains state-of-the-art hyperspectral unmixing performanceparticularly on the real datasets compared to the baseline techniques.

Improved Deep Spectral Convolution Network For Hyperspectral Unmixing With Multinomial Mixture Kernel and Endmember Uncertainty