35 citations · 60 across the 4 of their papers we have counts for
4 papers
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…
Sharpend Cosine Similarity based Neural Network for Hyperspectral Image Classification
Muhammad Ahmad
Hyperspectral Image Classification (HSIC) is a difficult task due to high inter and intra-class similarity and variability, nested regions, and overlapping. 2D Convolutional Neural…