Consistency Regularization Based on Masked Image Modeling for Semisupervised Remote Sensing Semantic Segmentation
Semisupervised semantic segmentation aims to effectively leverage both unlabeled and scare labeled images, reducing the reliance on labor-intensive pixel-level labeling for extensive training processes. The leading semisupervised learning method, consistency regularization, employs weak and strong d...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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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/10623542/ |