Unique Perturbation Methods Exploitation for Semi-Supervised Remote Sensing Image Semantic Segmentation
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| Publicado en: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences vol. X-G-2025 (2025), p. 1085 |
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| Autor principal: | |
| Otros Autores: | , , , , , |
| Publicado: |
Copernicus GmbH
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text - PDF |
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| Resumen: | Deep learning has significantly improved the accuracy of remote sensing semantic segmentation, yet its effectiveness is often constrained by the limited availability of annotated training samples. Semi-supervised learning (SSL) addresses this challenge by utilizing abundant unlabeled data, reducing dependence on manual annotations. However, current consistency regularization-based SSL methods, primarily developed for natural images, struggle to produce adequate perturbation diversity for robust model training in remote sensing image segmentation. In this work, we propose FusionMatch, a novel SSL framework featuring two perturbation mechanisms - NIRPerb and PSPerb - specifically designed for remote sensing imagery. NIRPerb utilizes near-infrared spectral data to enhance perturbation diversity. PSPerb adopts differentiated pan-sharpening fusion strategies to expand the perturbation space. Extensive experiments on both a building extraction dataset and a multi-class dataset demonstrate that FusionMatch outperforms state-of-the-art SSL methods in segmentation accuracy and robustness. |
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| ISSN: | 2194-9042 2194-9050 |
| DOI: | 10.5194/isprs-annals-X-G-2025-1085-2025 |
| Fuente: | Engineering Database |