A contrastive learning foundation model based on perfectly aligned sample pairs for remote sensing images
Self-supervised learning (SSL) facilitates the pre-training of foundation models without reliance on costly labeled data. Among SSL methods, contrastive learning (CL) excels at extracting robust semantic representations, even in the presence of complex interference. However, despite the success of C...
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| Autors principals: | , , , , |
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| Format: | Artigo |
| Idioma: | Inglês |
| Publicat: |
Taylor & Francis Group
2026-03-01
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| Col·lecció: | Geo-spatial Information Science |
| Matèries: | |
| Accés en línia: | https://www.tandfonline.com/doi/10.1080/10095020.2026.2628435 |
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