Pyramid Hierarchical Transformer for Hyperspectral Image Classification
This work addresses efficiency and scalability issues in HSIC, an incremental improvement for remote sensing and image analysis applications.
The paper tackles the challenge of variable-length input sequences in Hyperspectral Image Classification (HSIC) by proposing a pyramid-based hierarchical transformer (PyFormer), which organizes data hierarchically to enhance efficiency and capture local and global context, resulting in experimental superiority over traditional approaches with improved robustness through disjoint samples.
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.