Structured low-rank representation learning for hyperspectral sparse unmixing

ABSTRACT

Sparse unmixing methods have been widely used to estimate the abundance of each material component from hyperspectral images. However, conventional sparse unmixing approaches only consider matrix factorization without limited explorations on the high-dimensional structures of the third-order tensors. To address this issue, we propose SUnSLRR, a novel approach to sparse unmixing of hyperspectral data based on structured low-rank tensor modelling. In contrast to traditional methods, SUnSLRR leverages the low-rank property underlying the abundance tensor to exploit structural details from multiple modes. By incorporating sparsity regularization and a low-rank constraint, SUnSLRR can effectively extract the intrinsic features of hyperspectral data. We apply the alternating direction method of multipliers framework to solve the optimization problem induced by SUnSLRR, and experiments conducted on simulated and real hyperspectral images demonstrate the superior effectiveness of our proposed method compared to traditional methods in terms of both accuracy and efficiency.

KEYWORDS:

• Hyperspectral unmixing
• sparse unmixing
• sparse and low-rank representation
• hyperspectral analysis
• alternating direction method of multipliers (ADMM)

Acknowledgements

The authors would like to thank the Associate Editor who handled this manuscript and the anonymous reviewers for their outstanding comments and suggestions.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. Available online at http://speclab.cr.usgs.gov/PAPER/tetracorder

Additional information

Funding

This work was supported by supported by Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi (Grant no.2023L371), in part by supported by Lvliang City high-level talent introduction special (Grant no. 2022RC21), and in part by Graduate Research and Innovation Foundation of Chongqing, China, under Grant CYB22068, and in part by Shanxi Basic Research Plan (Free exploration) project (Grant no. 202303021212504).