A Self-Reinforcing Prototype Framework to Mitigate Pseudo-label Degradation in Semi-Supervised Remote Sensing Segmentation
Semi-supervised semantic segmentation has emerged as an effective strategy to alleviate the dependence on costly pixel-level annotations in remote sensing imagery by exploiting limited labeled data alongside abundant unlabeled samples. However, conventional confidence-based pseudo-labeling often pro...
Gardado en:
| Principais autores: | , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado: |
IEEE
2026-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Assuntos: | |
| Acceso en liña: | https://ieeexplore.ieee.org/document/11417825/ |
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