2 citations · 4 across the 5 of their papers we have counts for
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Cosine-Normalized Attention for Hyperspectral Image Classification
Muhammad Ahmad, Manuel Mazzara
Transformer-based methods have improved hyperspectral image classification (HSIC) by modeling long-range spatial-spectral dependencies; however, their attention mechanisms typicall…
3D Fourier-based Global Feature Extraction for Hyperspectral Image Classification
Muhammad Ahmad
Hyperspectral image classification (HSIC) has been significantly advanced by deep learning methods that exploit rich spatial-spectral correlations. However, existing approaches sti…
Dynamic Memory Transformer for Hyperspectral Image Classification
Muhammad Ahmad
Hyperspectral image (HSI) classification (HSIC) requires effective modeling of complex spatial-spectral dependencies under limited labeled data and high dimensionality. While trans…
EnergyFormer: Energy Attention with Fourier Embedding for Hyperspectral Image Classification
Saad Sohail, Muhammad Usama, Usman Ghous +3
Hyperspectral imaging (HSI) provides rich spectral-spatial information across hundreds of contiguous bands, enabling precise material discrimination in applications such as environ…
Hybrid State-Space and GRU-based Graph Tokenization Mamba for Hyperspectral Image Classification
Muhammad Ahmad, Muhammad Hassaan Farooq Butt, Muhammad Usama +4
Hyperspectral image (HSI) classification plays a pivotal role in domains such as environmental monitoring, agriculture, and urban planning. However, it faces significant challenges…
DiffFormer: a Differential Spatial-Spectral Transformer for Hyperspectral Image Classification
Muhammad Ahmad, Manuel Mazzara, Salvatore Distefano +2
Hyperspectral image classification (HSIC) has gained significant attention because of its potential in analyzing high-dimensional data with rich spectral and spatial information. I…