Dynamic Memory Transformer for Hyperspectral Image Classification
Fuente:
arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912969815752704 |
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| author | Ahmad, Muhammad |
| author_facet | Ahmad, Muhammad |
| contents | Hyperspectral image (HSI) classification (HSIC) requires effective modeling of complex spatial-spectral dependencies under limited labeled data and high dimensionality. While transformer-based models have shown strong capability in capturing long-range contextual information, they often introduce redundant attention patterns, which limits their effectiveness for fine-grained HSI analysis. To address these challenges, this paper proposes MemFormer, a lightweight transformer architecture for HSIC that incorporates a dynamic memory-enhanced attention mechanism. The proposed design augments multi-head self-attention with a compact global memory module that progressively aggregates contextual information across layers, enabling efficient modeling of long-range dependencies while reducing attention redundancy. In addition, a Spatial-Spectral Positional Embedding (SSPE) is used to jointly encode spatial continuity and spectral ordering, providing structurally consistent representations without relying on convolution-based positional encodings. Extensive experiments conducted on three benchmark hyperspectral datasets, including Indian Pines, WHU-Hi-HanChuan, and WHU-Hi-HongHu, demonstrate that MemFormer achieves superior classification performance compared to representative convolutional, hybrid, and transformer-based methods. On the Indian Pines dataset, MemFormer attains an overall accuracy of up to 99.55\%, average accuracy of 99.38\%, and a $κ$ coefficient of 99.49\%, highlighting its effectiveness and efficiency for HSIC. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_13242 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dynamic Memory Transformer for Hyperspectral Image Classification Ahmad, Muhammad Computer Vision and Pattern Recognition Hyperspectral image (HSI) classification (HSIC) requires effective modeling of complex spatial-spectral dependencies under limited labeled data and high dimensionality. While transformer-based models have shown strong capability in capturing long-range contextual information, they often introduce redundant attention patterns, which limits their effectiveness for fine-grained HSI analysis. To address these challenges, this paper proposes MemFormer, a lightweight transformer architecture for HSIC that incorporates a dynamic memory-enhanced attention mechanism. The proposed design augments multi-head self-attention with a compact global memory module that progressively aggregates contextual information across layers, enabling efficient modeling of long-range dependencies while reducing attention redundancy. In addition, a Spatial-Spectral Positional Embedding (SSPE) is used to jointly encode spatial continuity and spectral ordering, providing structurally consistent representations without relying on convolution-based positional encodings. Extensive experiments conducted on three benchmark hyperspectral datasets, including Indian Pines, WHU-Hi-HanChuan, and WHU-Hi-HongHu, demonstrate that MemFormer achieves superior classification performance compared to representative convolutional, hybrid, and transformer-based methods. On the Indian Pines dataset, MemFormer attains an overall accuracy of up to 99.55\%, average accuracy of 99.38\%, and a $κ$ coefficient of 99.49\%, highlighting its effectiveness and efficiency for HSIC. |
| title | Dynamic Memory Transformer for Hyperspectral Image Classification |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.13242 |