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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Detaylı Bibliyografya
Asıl Yazarlar:	Guoping Wang, Yan Dong, Gang Wan, Xinyu Zhao, Benkui Zhang, Keming Yang, Zhijuan Su
Materyal Türü:	Artigo
Dil:	Inglês
Baskı/Yayın Bilgisi:	IEEE 2025-01-01
Seri Bilgileri:	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Konular:	End-to-end few-shot classification hyperspectral image (HSI) semantic segmentation (SS) spatial–spectral attention mechanism Swin Transformer
Online Erişim:	https://ieeexplore.ieee.org/document/11164182/
Etiketler:	Etiketle Etiket eklenmemiş, İlk siz ekleyin!

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