Endmember-Guided Unmixing Network (EGU-Net): A General Deep Learning Framework for Self-Supervised Hyperspectral Unmixing

TL;DR

This paper introduces EGU-Net, a deep learning framework that leverages endmember information for improved hyperspectral unmixing, capable of handling spectral variability and spatial information for more accurate results.

Contribution

The paper proposes a novel endmember-guided deep learning network that integrates spectral and spatial information, enhancing unmixing accuracy and interpretability beyond existing methods.

Findings

EGU-Net outperforms state-of-the-art algorithms on multiple datasets.

The framework effectively models spectral variability and spatial context.

Codes are publicly available for reproducibility.

Abstract

Over the past decades, enormous efforts have been made to improve the performance of linear or nonlinear mixing models for hyperspectral unmixing, yet their ability to simultaneously generalize various spectral variabilities and extract physically meaningful endmembers still remains limited due to the poor ability in data fitting and reconstruction and the sensitivity to various spectral variabilities. Inspired by the powerful learning ability of deep learning, we attempt to develop a general deep learning approach for hyperspectral unmixing, by fully considering the properties of endmembers extracted from the hyperspectral imagery, called endmember-guided unmixing network (EGU-Net). Beyond the alone autoencoder-like architecture, EGU-Net is a two-stream Siamese deep network, which learns an additional network from the pure or nearly-pure endmembers to correct the weights of another unmixing network by sharing network parameters and adding spectrally meaningful constraints (e.g., non-negativity and sum-to-one) towards a more accurate and interpretable unmixing solution. Furthermore, the resulting general framework is not only limited to pixel-wise spectral unmixing but also applicable to spatial information modeling with convolutional operators for spatial-spectral unmixing. Experimental results conducted on three different datasets with the ground-truth of abundance maps corresponding to each material demonstrate the effectiveness and superiority of the EGU-Net over state-of-the-art unmixing algorithms. The codes will be available from the website: https://github.com/danfenghong/IEEE_TNNLS_EGU-Net.