Soft hypergraph regularized weighted low rank subspace clustering for hyperspectral image band selection

ABSTRACT

Recently, the graph regularized low-rank representation (GLRR) has been introduced in Hyperspectral Image (HSI) to explore global structures information by exploring the lowest-rank representation of all the data jointly and the local geometrical structure by the graph regularization. However, the traditional graph models are mostly based on a simple intrinsic structure. In this paper, to represent the complex intrinsic band information and further enhance the low rank of the matrix, we propose a soft hypergraph regularized weighted low-rank subspace clustering (HGWLRSC) method for HSI band selection. On the one hand, considering the complex correlation between adjacent bands, hypergraph technique is introduced, which take advantage of the band similarity properties to extract more valuable information and reveals the intrinsic multiple relationships of HSI band sets. On the other hand, the weighted low-rank subspace clustering model is introduced to not only capture the global structure information for the learned representation coefficient matrix but also to consider the importance of different rank components. The proposed algorithm was tested on three widely used hyperspectral data sets, and the experimental results indicate that the proposed HGWLRSC algorithm outperforms the other state-of-the art methods and achieves a very competitive band selection performance for HSI.

KEYWORDS:

• Band selection
• hyperspectral image
• hypergraph
• low rank subspace clustering

Disclosure statement

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

Author’s contribution

Conceptualization, J.X. and X.L.; methodology, X.Z.; software, G.Y.; validation, J.X., and X.Z.; formal analysis, M.A. and X.L.; writing – original draft preparation, J.X.; writing – review and editing, M.A. and X.L.; visualization, J.X.; supervision, G.Y. and X.Z.; project administration, X.L.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.

Additional information

Funding

This work was supported by Key research and development program of Shandong Province(2021JMRH0108), Natural Science Foundation of Shandong Province(ZR2020QF015), Collaborative innovation fund of Shandong Academy of Sciences of Qilu University of Technology (Shandong Academy of Science) (2020-CXY33 and 2020-CXY14), Major innovation fund of Qilu University of Technology (Shandong Academy of Science) (2022JBZ02-02), the National Natural Science Foundation of China under Grant (61802202), and China Postdoctoral Science Foundation (2022M711692).