Abstract
Hyperspectral image (HSI) classification faces challenges in diverse scenarios due to spectral-spatial complexity and class imbalance. Existing methods lack generalizability. This paper presents a novel Graph-Convolutional Networks with Adaptive Region Ensembles (GCN-ARE) framework. It integrates graph spectral learning, dynamic region subdivision, and classifier fusion. The key contributions are as follows: First, a normalized graph Laplacian operator ensures graph spectral stability, bounding the eigenvalue spectrum to stabilize feature propagation and address gradient issues in irregular terrains. Second, recursive K-means clustering under empirical risk bounds achieves adaptive region optimality, dynamically partitioning complex regions for enhanced local discriminability. Third, theoretical guarantees based on Hoeffding’s inequality enable dynamic ensemble consistency, facilitating optimal classifier selection under spatial-spectral uncertainty. Experiments on four HSI datasets (Botswana, Houston, Indian Pines, WHU-Hi-LongKou) show that GCN-ARE outperforms benchmarks like ViT and GAT, with average OA improvements of 1.5–5.7%. Ablation studies confirm the importance of adaptive subdivision and ensemble modules, and parameter sensitivity analyses reveal its robustness. The framework sets a new standard for robust HSI classification with its theoretical rigor and practical efficacy.
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Data availability
The datasets analyzed in this study are publicly available: the **Botswana** dataset from NASA’s EO-1 Hyperion mission (IEEE GRSS Data Fusion Contest), the **Houston** dataset from the IEEE GRSS 2013 Data Fusion Contest, the **Indian Pines** dataset from Purdue University’s MultiSpec, and the **WHU-Hi-LongKou** dataset released by Wuhan University. All datasets can be freely accessed for research purposes. Processed data and codes supporting the findings of this study are available from the corresponding author upon reasonable request.
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Acknowledgements
This was supported in by Natural Science Research Funds of Education Department of Anhui Province under Grant 2025AHGXZK40086, Spatial Information Acquisition and Application Joint Laboratory of Anhui Province, NO. 2024tlxykjxx03 and Talent Research Initiation Fund Project of Tongling University under Grant 2024tlxyrc039.
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Yutian Chen: Conceived the core idea of the GCN-ARE framework; implemented the algorithm design; conducted experiments on multiple hyperspectral datasets; analyzed the experimental results; drafted the initial manuscript; Hongliang Lu: Supervised the research; provided theoretical guidance on graph spectral stability, adaptive region subdivision, and ensemble learning; revised and refined the manuscript; secured project funding and coordinated collaboration among institutions; Xianglin Huang: Contributed to the validation experiments and comparative analysis; assisted in interpreting results; provided constructive feedback on the manuscript and contributed to improving its clarity and technical depth.
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Chen, Y., Lu, H. & Huang, X. Collaborative representation and confidence-driven semi-supervised learning for hyperspectral image classification. Sci Rep (2026). https://doi.org/10.1038/s41598-026-36806-6
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DOI: https://doi.org/10.1038/s41598-026-36806-6
