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Found 4 papers, 3 papers with code
Transformers Fusion across Disjoint Samples for Hyperspectral Image Classification
3D Swin Transformer (3D-ST) known for its hierarchical attention and window-based processing, excels in capturing intricate spatial relationships within images.
A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers
Traditional approaches encounter the curse of dimensionality, struggle with feature selection and extraction, lack spatial information consideration, exhibit limited robustness to noise, face scalability issues, and may not adapt well to complex data distributions.
Pyramid Hierarchical Transformer for Hyperspectral Image Classification
The traditional Transformer model encounters challenges with variable-length input sequences, particularly in Hyperspectral Image Classification (HSIC), leading to efficiency and scalability concerns.
Importance of Disjoint Sampling in Conventional and Transformer Models for Hyperspectral Image Classification
This paper presents an innovative disjoint sampling approach for training SOTA models on Hyperspectral image classification (HSIC) tasks.
