  Nonlinear hyperspectral unmixing has recently received considerable
attention, as linear mixture models do not lead to an acceptable resolution in
some problems. In fact, most nonlinear unmixing methods are designed by
assuming specific assumptions on the nonlinearity model which subsequently
limits the unmixing performance. In this paper, we propose an unsupervised
nonlinear unmixing approach based on deep learning by incorporating a general
nonlinear model with no special assumptions. This model consists of two
branches. In the first branch, endmembers are learned by reconstructing the
rows of hyperspectral images using some hidden layers, and in the second
branch, abundance values are learned based on the columns of respective images.
Then, using multi-task learning, we introduce an auxiliary task to enforce the
two branches to work together. This technique can be considered as a
regularizer mitigating overfitting, which improves the performance of the total
network. Extensive experiments on synthetic and real data verify the
effectiveness of the proposed method compared to some state-of-the-art
hyperspectral unmixing methods.
