Deep Nonlinear Hyperspectral Unmixing Using Multi-task Learning

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 …

assumptions attention cs.cv cs.lg cs.ne eess.iv linear multi-task learning paper performance unsupervised