5 papers
Multi-head Spatial-Spectral Mamba for Hyperspectral Image Classification
Muhammad Ahmad, Muhammad Hassaan Farooq Butt, Muhammad Usama +3
Spatial-Spectral Mamba (SSM) improves computational efficiency and captures long-range dependencies, addressing Transformer limitations. However, traditional Mamba models overlook…
Spatial and Spatial-Spectral Morphological Mamba for Hyperspectral Image Classification
Muhammad Ahmad, Muhammad Hassaan Farooq Butt, Adil Mehmood Khan +6
Recent advancements in transformers, specifically self-attention mechanisms, have significantly improved hyperspectral image (HSI) classification. However, these models often suffe…
Transformers Fusion across Disjoint Samples for Hyperspectral Image Classification
Muhammad Ahmad, Manuel Mazzara, Salvatore Distifano
3D Swin Transformer (3D-ST) known for its hierarchical attention and window-based processing, excels in capturing intricate spatial relationships within images. Spatial-spectral Tr…
Pyramid Hierarchical Transformer for Hyperspectral Image Classification
Muhammad Ahmad, Muhammad Hassaan Farooq Butt, Manuel Mazzara +1
The traditional Transformer model encounters challenges with variable-length input sequences, particularly in Hyperspectral Image Classification (HSIC), leading to efficiency and s…
Importance of Disjoint Sampling in Conventional and Transformer Models for Hyperspectral Image Classification
Muhammad Ahmad, Manuel Mazzara, Salvatore Distifano
Disjoint sampling is critical for rigorous and unbiased evaluation of state-of-the-art (SOTA) models. When training, validation, and test sets overlap or share data, it introduces…