{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T11:21:10Z","timestamp":1780053670383,"version":"3.54.0"},"reference-count":41,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2021,7,14]],"date-time":"2021-07-14T00:00:00Z","timestamp":1626220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFB2100501"],"award-info":[{"award-number":["2018YFB2100501"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"The Key Research and Development Program of Yunnan province in China","award":["2018IB023"],"award-info":[{"award-number":["2018IB023"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42090012, 41771452, 41771454 and 41890820"],"award-info":[{"award-number":["42090012, 41771452, 41771454 and 41890820"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Consulting research project of Chinese Academy of Engineering","award":["2020ZD16"],"award-info":[{"award-number":["2020ZD16"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The performance of deep learning is heavily influenced by the size of the learning samples, whose labeling process is time consuming and laborious. Deep learning algorithms typically assume that the training and prediction data are independent and uniformly distributed, which is rarely the case given the attributes and properties of different data sources. In remote sensing images, representations of urban land surfaces can vary across regions and by season, demanding rapid generalization of these surfaces in remote sensing data. In this study, we propose Meta-FSEO, a novel model for improving the performance of few-shot remote sensing scene classification in varying urban scenes. The proposed Meta-FSEO model deploys self-supervised embedding optimization for adaptive generalization in new tasks such as classifying features in new urban regions that have never been encountered during the training phase, thus balancing the requirements for feature classification tasks between multiple images collected at different times and places. We also created a loss function by weighting the contrast losses and cross-entropy losses. The proposed Meta-FSEO demonstrates a great generalization capability in remote sensing scene classification among different cities. In a five-way one-shot classification experiment with the Sentinel-1\/2 Multi-Spectral (SEN12MS) dataset, the accuracy reached 63.08%. In a five-way five-shot experiment on the same dataset, the accuracy reached 74.29%. These results indicated that the proposed Meta-FSEO model outperformed both the transfer learning-based algorithm and two popular meta-learning-based methods, i.e., MAML and Meta-SGD.<\/jats:p>","DOI":"10.3390\/rs13142776","type":"journal-article","created":{"date-parts":[[2021,7,14]],"date-time":"2021-07-14T21:56:51Z","timestamp":1626299811000},"page":"2776","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Meta-FSEO: A Meta-Learning Fast Adaptation with Self-Supervised Embedding Optimization for Few-Shot Remote Sensing Scene Classification"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2751-8294","authenticated-orcid":false,"given":"Yong","family":"Li","sequence":"first","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenfeng","family":"Shao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4323-382X","authenticated-orcid":false,"given":"Xiao","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Geosciences, University of Arkansas, Fayetteville, AR 72701, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4998-8796","authenticated-orcid":false,"given":"Bowen","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Song","family":"Peng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MGRS.2016.2540798","article-title":"Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art","volume":"4","author":"Zhang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"111425","DOI":"10.1016\/j.rse.2019.111425","article-title":"Deep Learning-Based Fusion of Landsat-8 and Sentinel-2 Images for a Harmonized Surface Reflectance Product","volume":"235","author":"Shao","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2811","DOI":"10.1109\/TGRS.2017.2783902","article-title":"When Deep Learning Meets Metric Learning: Remote Sensing Image Scene Classification via Learning Discriminative CNNs","volume":"56","author":"Cheng","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Shao, Z., Zhou, Z., Huang, X., and Zhang, Y. (2021). MRENet: Simultaneous Extraction of Road Surface and Road Centerline in Complex Urban Scenes from Very High-Resolution Images. Remote Sens., 13.","DOI":"10.3390\/rs13020239"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhang, R., Shao, Z., Huang, X., Wang, J., and Li, D. (2020). Object Detection in UAV Images via Global Density Fused Convolutional Network. Remote Sens., 12.","DOI":"10.3390\/rs12193140"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yao, H., Liu, Y., Wei, Y., Tang, X., and Li, Z. (2019). Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction. Proceedings of the The World Wide Web Conference on-WWW \u201919, ACM Press.","DOI":"10.1145\/3308558.3313577"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1986","DOI":"10.1109\/JSTARS.2020.2988477","article-title":"Classification of High-Spatial-Resolution Remote Sensing Scenes Method Using Transfer Learning and Deep Convolutional Neural Network","volume":"13","author":"Li","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1109\/LGRS.2020.2965558","article-title":"Classification of Large-Scale High-Resolution SAR Images With Deep Transfer Learning","volume":"18","author":"Huang","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Pires de Lima, R., and Marfurt, K. (2020). Convolutional Neural Network for Remote-Sensing Scene Classification: Transfer Learning Analysis. Remote Sens., 12.","DOI":"10.3390\/rs12010086"},{"key":"ref_11","first-page":"1126","article-title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","volume":"Volume 70","author":"Precup","year":"2017","journal-title":"Proceedings of the 34th International Conference on Machine Learning"},{"key":"ref_12","unstructured":"Chen, W.-Y., Liu, Y.-C., Kira, Z., Wang, Y.-C., and Huang, J.-B. (2019, January 6\u20139). A Closer Look at Few-Shot Classification. Proceedings of the International Conference on Learning Representations, New Orleans, LA, USA."},{"key":"ref_13","first-page":"1","article-title":"Generalizing from a Few Examples: A Survey on Few-Shot Learning","volume":"53","author":"Wang","year":"2020","journal-title":"ACM Comput. Surv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Hospedales, T., Antoniou, A., Micaelli, P., and Storkey, A. (2020). Meta-Learning in Neural Networks: A Survey. arXiv.","DOI":"10.1109\/TPAMI.2021.3079209"},{"key":"ref_15","unstructured":"Raghu, A., Raghu, M., Bengio, S., and Vinyals, O. (2020, January 30). Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML. Proceedings of the International Conference on Learning Representations, Addis Ababa, Ethiopia."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/3-540-44668-0_13","article-title":"Learning to Learn Using Gradient Descent","volume":"Volume 2130","author":"Dorffner","year":"2001","journal-title":"Artificial Neural Networks\u2014ICANN 2001"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Li, H., Cui, Z., Zhu, Z., Chen, L., Zhu, J., Huang, H., and Tao, C. (2020). RS-MetaNet: Deep Metametric Learning for Few-Shot Remote Sensing Scene Classification. IEEE Trans. Geosci. Remote Sens., 1\u201312.","DOI":"10.1109\/TGRS.2020.3027387"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ruswurm, M., Wang, S., Korner, M., and Lobell, D. (2020, January 14\u201316). Meta-Learning for Few-Shot Land Cover Classification. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00108"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A Survey on Transfer Learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_20","first-page":"201","article-title":"Why Does Unsupervised Pre-Training Help Deep Learning?","volume":"Volume 9","author":"Teh","year":"2010","journal-title":"Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics"},{"key":"ref_21","unstructured":"Saikia, T., Brox, T., and Schmid, C. (2020). Optimized Generic Feature Learning for Few-Shot Classification across Domains. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Chen, Z., Zhang, T., and Ouyang, C. (2018). End-to-End Airplane Detection Using Transfer Learning in Remote Sensing Images. Remote Sens., 10.","DOI":"10.3390\/rs10010139"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2022","DOI":"10.1109\/JSTARS.2016.2646138","article-title":"Iterative Reweighting Heterogeneous Transfer Learning Framework for Supervised Remote Sensing Image Classification","volume":"10","author":"Li","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chen, X., Xie, S., and He, K. (2021). An Empirical Study of Training Self-Supervised Visual Transformers. arXiv.","DOI":"10.1109\/ICCV48922.2021.00950"},{"key":"ref_25","unstructured":"Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R. (2017). Attention Is All You Need. Proceedings of the Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_26","first-page":"9","article-title":"Language Models Are Unsupervised Multitask Learners","volume":"1","author":"Radford","year":"2019","journal-title":"OpenAI Blog"},{"key":"ref_27","unstructured":"Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019). BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding. arXiv."},{"key":"ref_28","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An Image Is Worth 16x16 Words: Transformers for Image Recognition at Scale. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Srinivas, A., Lin, T.-Y., Parmar, N., Shlens, J., Abbeel, P., and Vaswani, A. (2021, January 19\u201325). Bottleneck Transformers for Visual Recognition. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01625"},{"key":"ref_30","unstructured":"Ravi, S., and Larochelle, H. (2017, January 24\u201326). Optimization as a model for few-shot learning. Proceedings of the International Conference on Learning Representations, Toulon, France."},{"key":"ref_31","unstructured":"Ren, M., Triantafillou, E., Ravi, S., Snell, J., Swersky, K., Tenenbaum, J.B., Larochelle, H., and Zemel, R.S. (2018). Meta-Learning for Semi-Supervised Few-Shot Classification. arXiv."},{"key":"ref_32","unstructured":"Dhillon, G.S., Chaudhari, P., Ravichandran, A., and Soatto, S. (2020). A Baseline for Few-Shot Image Classification. arXiv."},{"key":"ref_33","first-page":"1842","article-title":"Meta-Learning with Memory-Augmented Neural Networks","volume":"Volume 48","author":"Balcan","year":"2016","journal-title":"Proceedings of the 33rd International Conference on Machine Learning"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P.H.S., and Hospedales, T.M. (2018, January 18\u201322). Learning to Compare: Relation Network for Few-Shot Learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00131"},{"key":"ref_35","unstructured":"Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R. (2017). Prototypical Networks for Few-shot Learning. Advances in Neural Information Processing Systems 30, Curran Associates, Inc."},{"key":"ref_36","unstructured":"Wallach, H., Larochelle, H., Beygelzimer, A., dAlch\u00e9-Buc, F., Fox, E., and Garnett, R. (2019). Learning to Learn by Self-Critique. Proceedings of the Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_37","unstructured":"Rusu, A.A., Rao, D., Sygnowski, J., Vinyals, O., Pascanu, R., Osindero, S., and Hadsell, R. (2019, January 6\u20139). Meta-Learning with Latent Embedding Optimization. Proceedings of the International Conference on Learning Representations, New Orleans, LA, USA."},{"key":"ref_38","unstructured":"Li, Z., Zhou, F., Chen, F., and Li, H. (2017). Meta-SGD: Learning to Learn Quickly for Few-Shot Learning. arXiv."},{"key":"ref_39","unstructured":"Keskar, N.S., and Socher, R. (2017). Improving Generalization Performance by Switching from Adam to SGD. arXiv."},{"key":"ref_40","unstructured":"Metz, L., Maheswaranathan, N., Cheung, B., and Sohl-Dickstein, J. (2019). Meta-Learning Update Rules for Unsupervised Representation Learning. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Schmitt, M., Hughes, L.H., Qiu, C., and Zhu, X.X. (2019). SEN12MS\u2014A Curated Dataset of Georeferenced Multi-Spectral Sentinel-1\/2 Imagery for Deep Learning and Data Fusion. arXiv.","DOI":"10.5194\/isprs-annals-IV-2-W7-153-2019"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/14\/2776\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:30:42Z","timestamp":1760164242000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/14\/2776"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,14]]},"references-count":41,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["rs13142776"],"URL":"https:\/\/doi.org\/10.3390\/rs13142776","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,14]]}}}