Back to Search
Start Over
Semi-supervised semantic labeling of remote sensing images with improved image-level selection retraining
- Source :
- Alexandria Engineering Journal, Vol 94, Iss , Pp 235-247 (2024)
- Publication Year :
- 2024
- Publisher :
- Elsevier, 2024.
-
Abstract
- In recent years, image semantic segmentation technology has developed rapidly, but image annotation usually requires a significant amount of human and financial resources, especially for remote sensing image annotation, which can be expensive and sometimes even unaffordable. To address this issue, this paper integrates the idea of curriculum learning into the self-training method and screens reliable pseudo-labels through computing image-level confidence, significantly reducing the confirmation error problem. Furthermore, the semi-supervised model in this paper combines implicit semantic enhancement with strong data augmentation, which can reduce the coupling between the teacher model and the student model’s prediction distribution and enhance the model’s robustness. Finally, the proposed semi-supervised method is experimentally verified using the ISPRS competition dataset and compared with existing state-of-the-art (SOTA) methods. Experimental results show that the proposed semi-supervised segmentation method achieves higher segmentation accuracy compared to self-training methods. Moreover, despite not using iterative training to simplify the training process, the proposed method still yields satisfactory segmentation results.
Details
- Language :
- English
- ISSN :
- 11100168
- Volume :
- 94
- Issue :
- 235-247
- Database :
- Directory of Open Access Journals
- Journal :
- Alexandria Engineering Journal
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.0233f965be344769afda65ba0e02a51b
- Document Type :
- article
- Full Text :
- https://doi.org/10.1016/j.aej.2024.03.035