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Data Eng."},{"issue":"2","key":"10.1016\/j.knosys.2026.116350_b70","first-page":"203","article-title":"Optimization techniques for semi-supervised support vector machines","volume":"9","author":"Chapelle","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.knosys.2026.116350_b71","first-page":"10276","article-title":"Learning to self-train for semi-supervised few-shot classification","volume":"vol. 32","author":"Li","year":"2019"},{"key":"10.1016\/j.knosys.2026.116350_b72","doi-asserted-by":"crossref","unstructured":"Z. Yu, et al., Transmatch: a transfer-learning scheme for semi-supervised few-shot learning, in: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 12853\u201312861.","DOI":"10.1109\/CVPR42600.2020.01287"},{"key":"10.1016\/j.knosys.2026.116350_b73","first-page":"1","article-title":"Semi-supervised few-shot classification with multitask learning and iterative label correction","volume":"62","author":"Ji","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.knosys.2026.116350_b74","first-page":"298","article-title":"Structural high-resolution satellite image indexing","volume":"vol. 38","author":"Xia","year":"2010"},{"key":"10.1016\/j.knosys.2026.116350_b75","doi-asserted-by":"crossref","unstructured":"Y. Yang, S. Newsam, Bag-of-visual-words and spatial extensions for land-use classification, in: ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2010, pp. 270\u2013279.","DOI":"10.1145\/1869790.1869829"},{"issue":"10","key":"10.1016\/j.knosys.2026.116350_b76","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.1109\/JPROC.2017.2675998","article-title":"Remote sensing image scene classification: benchmark and state of the art","volume":"105","author":"Cheng","year":"2017","journal-title":"Proc. IEEE"},{"key":"10.1016\/j.knosys.2026.116350_b77","first-page":"1","article-title":"Attention-based contrastive learning for few-shot remote sensing image classification","volume":"62","author":"Xu","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.knosys.2026.116350_b78","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.isprsjprs.2025.05.026","article-title":"Few-shot remote sensing scene classification via parameter-free attention and region matching","volume":"227","author":"Jia","year":"2025","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"issue":"55","key":"10.1016\/j.knosys.2026.116350_b79","first-page":"1","article-title":"GDPNet: gated dynamic prototype network for few-shot remote sensing scene classification","volume":"22","author":"Liu","year":"2025","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"10.1016\/j.knosys.2026.116350_b80","doi-asserted-by":"crossref","first-page":"9566","DOI":"10.1109\/JSTARS.2025.3551599","article-title":"Local-descriptors-based rectification network for few-shot remote sensing scene classification","volume":"18","author":"Qin","year":"2025","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"10.1016\/j.knosys.2026.116350_b81","first-page":"1","article-title":"Personalized multiparty few-shot learning for remote sensing scene classification","volume":"62","author":"Wang","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.knosys.2026.116350_b82","first-page":"1","article-title":"Multiform ensemble self-supervised learning for few-shot remote sensing scene classification","volume":"61","author":"Li","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.knosys.2026.116350_b83","doi-asserted-by":"crossref","unstructured":"K. Cheng, et al., TeAw: text-aware few-shot remote sensing image scene classification, in: IEEE International Conference on Acoustics, Speech and Signal Processing, 2023, pp. 1\u20135.","DOI":"10.1109\/ICASSP49357.2023.10095523"},{"issue":"9","key":"10.1016\/j.knosys.2026.116350_b84","doi-asserted-by":"crossref","first-page":"2337","DOI":"10.1007\/s11263-022-01653-1","article-title":"Learning to prompt for vision-language models","volume":"130","author":"Zhou","year":"2022","journal-title":"Int. J. Comput. Vis."},{"issue":"2","key":"10.1016\/j.knosys.2026.116350_b85","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1007\/s11263-023-01891-x","article-title":"Clip-adapter: better vision-language models with feature adapters","volume":"132","author":"Gao","year":"2024","journal-title":"Int. J. Comput. 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