Hyperspectral image (HSI) classification remains a challenging task due to the intricate spatial-spectral correlations. Existing transformer models excel in capturing long-range dependencies but often suffer from information redundancy and attention inefficiencies, limiting their ability to model fine-grained relationships crucial for HSI classification. To overcome these limitations, this work proposes MemFormer, a lightweight and memory-enhanced transformer. MemFormer introduces a memory-enhanced multi-head attention mechanism that iteratively refines a dynamic memory module, enhancing feature extraction while reducing redundancy across layers. Additionally, a dynamic memory enrichment strategy progressively captures complex spatial and spectral dependencies, leading to more expressive feature representations. To further improve structural consistency, we incorporate a spatial-spectral positional encoding (SSPE) tailored for HSI data, ensuring continuity without the computational burden of convolution-based approaches. Extensive experiments on benchmark datasets demonstrate that MemFormer achieves superior classification accuracy, outperforming state-of-the-art methods.
View on arXiv@article{ahmad2025_2504.13242,
title={ Dynamic Memory-enhanced Transformer for Hyperspectral Image Classification },
author={ Muhammad Ahmad and Manuel Mazzara and Salvatore Distefano and Adil Mehmood Khan },
journal={arXiv preprint arXiv:2504.13242},
year={ 2025 }
}