Deep Nonlinear Hyperspectral Unmixing Using Multi-task Learning
This work addresses limitations in hyperspectral image analysis for remote sensing applications, offering an incremental improvement over existing nonlinear unmixing methods.
The paper tackles the problem of nonlinear hyperspectral unmixing by proposing an unsupervised deep learning approach that uses a general nonlinear model without special assumptions, incorporating multi-task learning to improve performance, and demonstrates effectiveness through experiments on synthetic and real data.
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.