93 citations · 264 across the 11 of their papers we have counts for
11 papers
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…
Transformer-Driven Active Transfer Learning for Cross-Hyperspectral Image Classification
Muhammad Ahmad, Francesco Mauro, Manuel Mazzara +3
Hyperspectral image (HSI) classification presents inherent challenges due to high spectral dimensionality, significant domain shifts, and limited availability of labeled data. To a…
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…
WaveMamba: Spatial-Spectral Wavelet Mamba for Hyperspectral Image Classification
Muhammad Ahmad, Muhammad Usama, Manuel Mazzara +1
Hyperspectral Imaging (HSI) has proven to be a powerful tool for capturing detailed spectral and spatial information across diverse applications. Despite the advancements in Deep L…
A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers and Mamba Models
Muhammad Ahmad, Salvatore Distifano, Adil Mehmood Khan +7
Hyperspectral Image Classification (HSC) presents significant challenges owing to the high dimensionality and intricate nature of Hyperspectral (HS) data. While traditional Machine…