Spectral-spatial adversarial network for nonlinear hyperspectral unmixing of imbalanced datasets
With its successful application in various fields, hyperspectral unmixing (HU) technology has received extensive attention in remote sensing processing.Recently, various autoencoders based on the linear mixing model (LMM) have been proposed to provide a feasible unsupervised solution for HU.However, the ability of autoencoders to exploit the prior