Graph Transformer With Structural Embedding and Training for Hyperspectral Image

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Abstract
Graph transformer networks have received more attention in hyperspectral image (HSI) classification. However, they overlooked the influence of graph connectivity strength in positional encoding and distribution. In order to address the above deficiencies, we proposed the novel graph transformer with structural embedding and training (GTSET) for HSI classification. Specifically, the structural embedding module firstly aimed at extracting effectively local and non-local feature information via patch-based distance encoding and centrality correlation coefficients based on graph connectivity strength, alleviating spectral variability. Secondly, the structural training module aimed at addressing imbalanced structural position distribution of labeled samples by leveraging the topological graph connectivity to determine their structural position distribution and reweighting the influence of labeled samples on the graph transformer training stage, exploring the guiding role of labeled samples in low spatial resolution of HSI. Next, we further refine training weights based on the spectral feature smoothness of labeled samples. Finally, comprehensive experiments on three real-world HSI datasets demonstrate that the GTSET achieves superior performance in HSI classification with limited labeled samples, compared to other popular classification methods. Implementation of GTSET, along with examples, can be found on the GitHub repository: https://github.com/xuchengchao0/GTSET.
Funding Information
  • National Natural Science Foundation of China (62302002)
  • Key Program of Natural Science Project of Educational Commission of Anhui Province (2023AH050080)
  • National Science and Technology Infrastructure Program (202423l10050004)

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