Dynamic Memory-enhanced Transformer for Hyperspectral Image Classification

• URL: http://arxiv.org/abs/2504.13242v1
• Date: Thu, 17 Apr 2025 17:43:34 GMT
• Title: Dynamic Memory-enhanced Transformer for Hyperspectral Image Classification
• Authors: Muhammad Ahmad, Manuel Mazzara, Salvatore Distefano, Adil Mehmood Khan,
• Abstract summary: Hyperspectral image (HSI) classification remains a challenging task due to the intricate spatial-spectral correlations.<n>Existing transformer models excel in capturing long-range dependencies but often suffer from information redundancy and attention inefficiencies.<n>MemFormer introduces a memory-enhanced multi-head attention mechanism that iteratively refines a dynamic memory module.<n>A dynamic memory enrichment strategy progressively captures complex spatial and spectral dependencies, leading to more expressive feature representations.
• Score: 3.5093938502961763
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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