Cycle and Self-Supervised Consistency Training for Adapting Semantic Segmentation of Aerial Images
Semantic segmentation is a critical problem for many remote sensing (RS) image applications. Benefiting from large-scale pixel-level labeled data and the continuous evolution of deep neural network architectures, the performance of semantic segmentation approaches has been constantly improved. Howev...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-03-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/7/1527 |