RIHP-Net: Rotation-Invariant Hyperspectral Image Classification via Harmonic Fiber Pooling and Rotation-Conditioned Spectral Fusion
Hyperspectral image classification can degrade under in-plane rotations because spatial structures change orientation and the learned spectral–spatial coupling in patch-based models becomes less stable. Most existing approaches improve robustness mainly through data augmentation, without expl...
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| Principais autores: | , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
IEEE
2026-01-01
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| coleção: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Assuntos: | |
| Acesso em linha: | https://ieeexplore.ieee.org/document/11534556/ |
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