Spatial-spectral graph convolutional extreme learning machine for hyperspectral image classification

ABSTRACT

Hyperspectral image has excellent spectral information and abundant spatial information, and its feature quality is one of the key factors affecting the classification performance. Extreme learning machine (ELM) is widely used in hyperspectral classification problems. However, traditional ELM is usually based on regular Euclidean data, ignoring the inherent structured information between hyperspectral pixels, resulting in poor robustness. In this paper, we propose a new supervised extreme learning machine framework, termed spatial-spectral graph convolutional ELM (SSG-ELM), based on using graph convolution to extend ELM into the non-Euclidean domain. The method inherits all the advantages from ELM, and consists of a random graph convolutional layer followed by a graph convolutional regression layer, enabling it to model complex intra class variations. Otherwise, a local spectral-spatial context integration and reshaping mechanism is incorporated into the hidden layer feature representation by using a context-aware bilateral filtering procedure. Experimental results show that the proposed algorithm obtains a competitive performance and outperforms other state-of-the-art ELM-based methods.

KEYWORDS:

• Hyperspectral image
• supervised classification
• graph convolutional network
• extreme learning machine (ELM)

Disclosure statement

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

Author contributions

Conceptualization, J.X. and X.L.; methodology, X.Z.; software, G.Y.; validation, H.Z., and X.Z.; formal analysis, M.A. and X.L.; writing – original draft preparation, J.X.; writing – review and editing, H.Z. and X.L.; visualization, J.X.; supervision, H.Z. 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 Major innovation fund of Qilu University of Technology [Shandong Academy of Science] [2022JBZ02-02], Key research and development program of Shandong Province [2021JMRH0108], Basic research project of science, education and production integration pilot project [2023PX061], Talent research projects in universities [colleges] [2023RCKY156], Science and technology program of Jinan [202214001]. [Corresponding author: Jinhuan Xu.]