A Remote Sensing Semantic Self-Supervised Segmentation Model Integrating Local Sensitivity and Global Invariance
Self-supervised semantic segmentation is a crucial approach for addressing the issue of insufficient labeled data. However, traditional self-supervised learning methods designed for natural images are often unsuitable for remote sensing images, as they struggle to capture both local and global infor...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11012709/ |
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