Multimodal self-supervised learning for semantic analysis of PolSAR imagery
Author:
Affiliation:
1. Technical University of Berlin,Berlin,Germany
2. German Aerospace Center (DLR),Weßling,Germany
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10281394/10281399/10283301.pdf?arnumber=10283301
Reference24 articles.
1. A simple framework for contrastive learning of visual representations;chen;International Conference on Machine Learning,2020
2. Global Land-Cover Mapping With Weak Supervision: Outcome of the 2020 IEEE GRSS Data Fusion Contest
3. Momentum contrast for unsupervised visual representation learning;he;Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,2020
4. 2020 IEEE GRSS Data Fusion Contest: Global Land Cover Mapping With Weak Supervision [Technical Committees]
5. Exploring Vision Transformers for Polarimetric SAR Image Classification;dong;IEEE Transactions on Geoscience and Remote Sensing,2021
Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. PolSAR Image Classification Via a Multigranularity Hybrid CNN-ViT Model With External Tokens and Cross-Attention;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024
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