End-to-End 3-D Transformer Semantic Segmentation Architecture for Few-Shot Classification With Hyperspectral Multiscale Spatial–Spectral Attention

The application of hyperspectral image (HSI) has expanded from Earth observation in large scenes to medical and industrial detection in small scenes. However, there are still many challenges in high-precision few-shot classification with high spectral similarity of targets. We have designed an end-t...

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Autors principals:	Guoping Wang, Yan Dong, Gang Wan, Xinyu Zhao, Benkui Zhang, Keming Yang, Zhijuan Su
Format:	Artigo
Idioma:	Inglês
Publicat:	IEEE 2025-01-01
Col·lecció:	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Matèries:	End-to-end few-shot classification hyperspectral image (HSI) semantic segmentation (SS) spatial–spectral attention mechanism Swin Transformer
Accés en línia:	https://ieeexplore.ieee.org/document/11164182/
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End-to-End 3-D Transformer Semantic Segmentation Architecture for Few-Shot Classification With Hyperspectral Multiscale Spatial&#x2013;Spectral Attention

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End-to-End 3-D Transformer Semantic Segmentation Architecture for Few-Shot Classification With Hyperspectral Multiscale Spatial–Spectral Attention