Pyramid Hierarchical Transformer for Hyperspectral Image Classification
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866929505437745152 |
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| author | Ahmad, Muhammad Butt, Muhammad Hassaan Farooq Mazzara, Manuel Distifano, Salvatore |
| author_facet | Ahmad, Muhammad Butt, Muhammad Hassaan Farooq Mazzara, Manuel Distifano, Salvatore |
| contents | The traditional Transformer model encounters challenges with variable-length input sequences, particularly in Hyperspectral Image Classification (HSIC), leading to efficiency and scalability concerns. To overcome this, we propose a pyramid-based hierarchical transformer (PyFormer). This innovative approach organizes input data hierarchically into segments, each representing distinct abstraction levels, thereby enhancing processing efficiency for lengthy sequences. At each level, a dedicated transformer module is applied, effectively capturing both local and global context. Spatial and spectral information flow within the hierarchy facilitates communication and abstraction propagation. Integration of outputs from different levels culminates in the final input representation. Experimental results underscore the superiority of the proposed method over traditional approaches. Additionally, the incorporation of disjoint samples augments robustness and reliability, thereby highlighting the potential of our approach in advancing HSIC.
The source code is available at https://github.com/mahmad00/PyFormer. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_14945 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Pyramid Hierarchical Transformer for Hyperspectral Image Classification Ahmad, Muhammad Butt, Muhammad Hassaan Farooq Mazzara, Manuel Distifano, Salvatore Computer Vision and Pattern Recognition The traditional Transformer model encounters challenges with variable-length input sequences, particularly in Hyperspectral Image Classification (HSIC), leading to efficiency and scalability concerns. To overcome this, we propose a pyramid-based hierarchical transformer (PyFormer). This innovative approach organizes input data hierarchically into segments, each representing distinct abstraction levels, thereby enhancing processing efficiency for lengthy sequences. At each level, a dedicated transformer module is applied, effectively capturing both local and global context. Spatial and spectral information flow within the hierarchy facilitates communication and abstraction propagation. Integration of outputs from different levels culminates in the final input representation. Experimental results underscore the superiority of the proposed method over traditional approaches. Additionally, the incorporation of disjoint samples augments robustness and reliability, thereby highlighting the potential of our approach in advancing HSIC. The source code is available at https://github.com/mahmad00/PyFormer. |
| title | Pyramid Hierarchical Transformer for Hyperspectral Image Classification |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.14945 |