EMLM-Net: An Extended Multilinear Mixing Model-Inspired Dual-Stream Network for Unsupervised Nonlinear Hyperspectral Unmixing
Minglei Li, Bin Yang, Bin Wang
Abstract
To mitigate the impact of mixed pixels in hyperspectral images (HSIs), substantial progress has been made in both model- and deep learning-based unmixing methods. However, issues such as complex computational processes and limited interpretability, hinder the improvement of their unmixing performance. Particularly, unsupervised nonlinear hyperspectral unmixing (HU) remains a great challenge. In this paper, we propose an extended multilinear mixing (EMLM) model-inspired dual-stream network for unsupervised nonlinear HU. Firstly, the alternating direction method of multipliers (ADMM) algorithm for the EMLM-based unmixing problem is unfolded to construct an encoder network. Subsequently, it is connected to a decoder network derived from the EMLM, creating an autoencoder-like network architecture. Secondly, the original HSIs and superpixel-averaging-based coarse HSIs are input into two network branches with identical architectures, respectively, to build a novel weight-sharing dual-stream network. Furthermore, estimates of abundances and nonlinear parameters obtained from the two branches are utilized to formulate local spatial similarity regularizers, enhancing the network’s loss function and effectively improving unmixing accuracy. Finally, experiments conducted on the laboratory-created dataset and real-world datasets validate that the proposed method exhibits superior unmixing performance compared to state-of-the-art methods. In addition, our code is available at: https://github.com/I3ab/EMLM-Net.