collaborators

6 papers

cs.CV2025

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…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2024

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…

cs.CV2024

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…

cs.CV2024

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…