Open Access

ARTICLE

FEAM-Swin: A Lightweight Frequency Aware Swin Transformer for Efficient Hyperspectral Image Classification

Farhan Ullah¹, Irfan Ullah², Khalil Khan³, Sarra Ayouni⁴, Quan Wang^1,*

1 School of Internet of Things Engineering, Wuxi University, Wuxi, China
2 School of Computer Science, Chengdu University of Technology, Sichuan, China
3 Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia
4 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

* Corresponding Author: Quan Wang. Email:

(This article belongs to the Special Issue: Machine Learning and Deep Learning-Based Pattern Recognition, 2nd Edition)

Computer Modeling in Engineering & Sciences 2026, 148(3), 36 https://doi.org/10.32604/cmes.2026.087860

Received 24 June 2026; Accepted 02 September 2026; Issue published 28 September 2026

Abstract

Hyperspectral image (HSI) classification requires models that can effectively capture long-range contextual dependencies while preserving fine-grained spectral–spatial variations under strict computational constraints. Recent transformer-based approaches, particularly Swin Transformers, have shown strong performance by leveraging localized self-attention; however, their reliance on generic attention mechanisms often overlooks frequency-sensitive information that is critical for discriminating spectrally similar materials. Moreover, existing frequency-aware designs typically introduce heavy parameterization or explicit spectral transforms, limiting their efficiency and practical deployment. In this paper, we propose FEAM-Swin, a lightweight frequency-aware Swin Transformer designed for efficient HSI classification. The proposed model introduces a novel Frequency-Enhanced Attention Modulator (FEAM), which captures local spectral–spatial frequency variations via gradient-based energy estimation and performs adaptive channel modulation with minimal computational overhead. Unlike conventional frequency modelling approaches, FEAM avoids explicit Fourier or wavelet transforms, enabling seamless integration into hierarchical transformer architectures. FEAM is embedded within Swin Transformer blocks to enhance discriminative feature learning while preserving the efficiency of window-based self-attention. Extensive experiments performed on four benchmark hyperspectral datasets widely used in the research community, including Indian Pines, University of Pavia, Salinas and KSC, demonstrate that FEAM-Swin consistently outperforms state-of-the-art CNN- and transformer-based methods. Specifically, FEAM-Swin achieves overall accuracies of 98.17%, 99.87%, 99.97%, and 98.41%, respectively, yielding consistent improvements over competing approaches while requiring fewer parameters and lower computational complexity. These results validate the effectiveness of lightweight frequency-aware modulation and establish FEAM-Swin as a practical and robust solution for real-world HSI classification.

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