IVIU-Net: Implicit Variable Iterative Unrolling Network for Hyperspectral Sparse Unmixing

At present, an emerging technique called the algorithm unrolling approach has attracted wide attention, because it is capable of developing efficient and interpretable layers to eliminate the black-box nature of deep learning (DL). In this article, inspired by the sparse unmixing model, we propose a model-driven DL approach, namely, an implicit variable iterative unrolling network (IVIU-Net). First of all, the unmixing performance and adaptive ability of the model are enhanced by introducing learnable parameters into the sparse unmixing algorithm. Then, a specific spatial convolution module is integrated into the network to promote the smoothness of the latent abundance map. Finally, a comprehensive loss function with three terms such as average spectral angle distance, hyperspectral images reconstruction error, and spectral information divergence, is presented to train the IVIU-Net in an unsupervised way. Compared to the unmixing results of most existing data-driven DL algorithms, our network has significant advantages in two folds: it is able to achieve better stability instead of relying heavily on the endmember initialization results and it has better interpretability and robustness in the unmixing procedure. Experimental results on synthetic and real data show that the proposed network outperforms the state-of-the-art in terms of better convergence, faster unmixing speed as well as better accuracy.

Automated summary

This article introduces a model-driven deep learning approach called IVIU-Net, which enhances the adaptability and stability of the model by introducing learnable parameters and a specific spatial convolution module. A comprehensive loss function is proposed to train the IVIU-Net in an unsupervised way. Experimental results show that the proposed network outperforms existing data-driven deep learning algorithms in terms of convergence, speed, and accuracy.