SFSCDNet: A Deep Learning Model With Spatial Flow-Based Semantic Change Detection From Bi-Temporal Satellite Images
Semantic change detection in remote sensing imagery plays a pivotal role in urban planning, environmental monitoring, and disaster assessment applications. Existing deep learning-based methods, particularly those relying on triple-branch architectures, often struggle to accurately localize and predi...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10807279/ |
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