Pyramid Hierarchical Transformer for Hyperspectral Image Classification

Abstract

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.

References in corpus (4)

• Graph Convolutional Networks for Hyperspectral Image Classification
• SpectralGPT: Spectral Remote Sensing Foundation Model
• Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects
• Spatial Gated Multi-Layer Perceptron for Land Use and Land Cover Mapping

Cited by in corpus (9)

• A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers and Mamba Models
• Spatial and Spatial-Spectral Morphological Mamba for Hyperspectral Image Classification
• WaveMamba: Spatial-Spectral Wavelet Mamba for Hyperspectral Image Classification
• Multi-head Spatial-Spectral Mamba for Hyperspectral Image Classification
• DiffFormer: a Differential Spatial-Spectral Transformer for Hyperspectral Image Classification
• Transformers Fusion across Disjoint Samples for Hyperspectral Image Classification
• Transformer-Driven Active Transfer Learning for Cross-Hyperspectral Image Classification
• EnergyFormer: Energy Attention with Fourier Embedding for Hyperspectral Image Classification
• AMBER -- Advanced SegFormer for Multi-Band Image Segmentation: an application to Hyperspectral Imaging