Pyramid Hierarchical Spatial-Spectral Transformer for Hyperspectral Image Classification

Ahmad, Muhammad; Butt, Muhammad Hassaan Farooq; Mazzara, Manuel; Distefano, Salvatore; Khan, Adil Mehmood; Altuwaijri, Hamad Ahmed

Abstract

The Transformer model encounters challenges with variable-length input sequences, leading to efficiency and scalability concerns. To overcome this, we propose a pyramid-based hierarchical Spatial-Spectral Transformer (PyFormer). This innovative approach organizes input data hierarchically into pyramid segments, each representing distinct abstraction levels, thereby enhancing processing efficiency. At each level, a dedicated Transformer encoder is applied, effectively capturing both local and global context. Integration of outputs from different levels culminates in the final input representation. In short, the Pyramid excels at capturing spatial features and local patterns, while the Transformer effectively models spatial-spectral correlations and long-range dependencies. Experimental results underscore the superiority of the proposed method over state-of-the-art (SOTA) approaches, achieving overall accuracies of 96.28% for the Pavia University dataset and 97.36% for the University of Houston dataset. Additionally, the incorporation of disjoint samples augments robustness and reliability, thereby highlighting the potential of PyFormer in advancing HSIC.
The source code is available at https://github.com/mahmad00/PyFormer.

Citation

Ahmad, M., Butt, M. H. F., Mazzara, M., Distefano, S., Khan, A. M., & Altuwaijri, H. A. (2024). Pyramid Hierarchical Spatial-Spectral Transformer for Hyperspectral Image Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 17681-17689. https://doi.org/10.1109/jstars.2024.3461851

• Journal Article Type: Article
• Acceptance Date: Sep 13, 2024
• Online Publication Date: Sep 17, 2024
• Publication Date: Jan 1, 2024
• Deposit Date: Sep 19, 2024
• Publicly Available Date: Oct 15, 2024
• Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Print ISSN: 1939-1404
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 17
• Pages: 17681-17689
• DOI: https://doi.org/10.1109/jstars.2024.3461851
• Keywords: Pyramid network; Spatial-spectral transformer (SST); Hyperspectral image classification (HSIC)
• Public URL: https://hull-repository.worktribe.com/output/4832972