93 citations · 248 across the 8 of their papers we have counts for
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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…
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…
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…