Title: Hybrid convolutional network with adaptive graph attention mechanism for HSI classification

Authors: Sandeep; Shashidhar Sonnad

Addresses: Department of Electronics and Communication Engineering, Sharnbasva University, Kalaburagi, Karnataka, India ' Department of Electronics and Communication Engineering, Sharnbasva University, Kalaburagi, Karnataka, India

Abstract: This research introduces a novel hyperspectral image classification (HSIC) model, named 3D-2D hybrid convolution and enhanced graph attention mechanism (HCN-AMGAM), designed to overcome the challenges of high-dimensional spectral-spatial feature extraction and classification accuracy. HSI captures rich spectral information across multiple wavelengths, enabling precise identification of diverse ground objects. The proposed HCN-AMGAM integrates 3D-2D hybrid convolution with an enhanced graph attention mechanism to jointly exploit spatial continuity and spectral correlation. The hybrid convolution module effectively captures local-global dependencies while reducing computational complexity compared to conventional 3D CNNs. An adaptive graph construction strategy based on deep spectral-spatial embeddings strengthen feature connectivity, and the multi-feature fusion GAM enhances information integration while minimising redundancy. This synergy leads to more discriminative and robust hyperspectral representations. The proposed end-to-end framework not only achieves higher accuracy but also enhances model interpretability and scalability for real-world remote sensing tasks. Experiments conducted on Kennedy Space Center (KSC) and Pavia University (PU) datasets demonstrate that HCN-AMGAM achieves state-of-the-art performance, confirming its significant impact on advancing hyperspectral image classification and environmental monitoring applications.

Keywords: hyperspectral image; HSIs; convolutional neural networks; CNNs; Pavia University; PU; Kennedy Space Center; KSC; graph attention mechanism; GAM.

DOI: 10.1504/IJCSE.2026.155099

International Journal of Computational Science and Engineering, 2026 Vol.29 No.4, pp.408 - 416

Received: 28 May 2025
Accepted: 01 Feb 2026
Published online: 27 Jul 2026 *

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