Universal Domain Adaptation without Source Data for Remote Sensing Image Scene Classification
Author:
Affiliation:
1. Technical University of Munich (TUM),Data Science in Earth Observation,Munich,Germany
2. Technical University of Munich (TUM),Chair of Remote Sensing Technology (LMF),Munich,Germany
Funder
China Scholarship Council
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9883023/9883024/09884889.pdf?arnumber=9884889
Reference5 articles.
1. Model Adaptation: Unsupervised Domain Adaptation Without Source Data
2. Universal source-free domain adaptation;kundu;Proceedings of the IEEE/CVF Conference on Comput-er Vision and Pattern Recognition,0
3. Universal domain adap-tation;you;Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,0
4. Class-aware domain adaptation for semantic segmentation of remote sensing images;xu;IEEE transactions on geo-science and remote sensing,2020
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1. Domain Adaptive Remote Sensing Scene Classification With Middle-Layer Feature Extraction and Nuclear Norm Maximization;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024
2. C³DA: A Universal Domain Adaptation Method for Scene Classification From Remote Sensing Imagery;IEEE Geoscience and Remote Sensing Letters;2024
3. Advancements in cross-domain remote sensing scene interpretation;Journal of Image and Graphics;2024
4. Universal Domain Adaptation for Remote Sensing Image Scene Classification;IEEE Transactions on Geoscience and Remote Sensing;2023
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