{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T11:38:43Z","timestamp":1775043523764,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T00:00:00Z","timestamp":1675209600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China under Grant under Grant","award":["2021YFE0194700"],"award-info":[{"award-number":["2021YFE0194700"]}]},{"name":"National Key R&amp;D Program of China under Grant under Grant","award":["HZ2021008"],"award-info":[{"award-number":["HZ2021008"]}]},{"name":"Chongqing municipal education commission","award":["2021YFE0194700"],"award-info":[{"award-number":["2021YFE0194700"]}]},{"name":"Chongqing municipal education commission","award":["HZ2021008"],"award-info":[{"award-number":["HZ2021008"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Scene classification is a critical technology to solve the challenges of image search and image recognition. It has become an indispensable and challenging research topic in the field of remote sensing. At present, most scene classifications are solved by deep neural networks. However, existing methods require large-scale training samples and are not suitable for actual scenarios with only a few samples. For this reason, a framework based on metric learning and local descriptors (MLLD) is proposed to enhance the classification effect of remote sensing scenes on the basis of few-shot. Specifically, MLLD adopts task-level training that is carried out through meta-learning, and meta-knowledge is learned to improve the model\u2019s ability to recognize different categories. Moreover, Manifold Mixup is introduced by MLLD as a feature processor for the hidden layer of deep neural networks to increase the low confidence space for smoother decision boundaries and simpler hidden layer representations. In the end, a learnable metric is introduced; the nearest category of the image is matched by measuring the similarity of local descriptors. Experiments are conducted on three public datasets: UC Merced, WHU-RS19, and NWPU-RESISC45. Experimental results show that the proposed scene classification method can achieve the most advanced results on limited datasets.<\/jats:p>","DOI":"10.3390\/rs15030831","type":"journal-article","created":{"date-parts":[[2023,2,2]],"date-time":"2023-02-02T03:47:33Z","timestamp":1675309653000},"page":"831","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Few-Shot Remote Sensing Image Scene Classification Based on Metric Learning and Local Descriptors"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1668-6014","authenticated-orcid":false,"given":"Zhengwu","family":"Yuan","sequence":"first","affiliation":[{"name":"Chongqing Engineering Research Center for Spatial Big Data Intelligent Technology, School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9515-8465","authenticated-orcid":false,"given":"Chan","family":"Tang","sequence":"additional","affiliation":[{"name":"Chongqing Engineering Research Center for Spatial Big Data Intelligent Technology, School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"given":"Aixia","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0557-8912","authenticated-orcid":false,"given":"Wendong","family":"Huang","sequence":"additional","affiliation":[{"name":"Chongqing Engineering Research Center for Spatial Big Data Intelligent Technology, School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"given":"Wang","family":"Chen","sequence":"additional","affiliation":[{"name":"Chongqing Engineering Research Center for Spatial Big Data Intelligent Technology, School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bosch, A., Zisserman, A., and Munoz, X. (2006). Scene Classification via pLSA, Springer. European Conference on Computer Vision.","DOI":"10.1007\/11744085_40"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3735","DOI":"10.1109\/JSTARS.2020.3005403","article-title":"Remote sensing image scene classification meets deep learning: Challenges, methods, benchmarks, and opportunities","volume":"13","author":"Cheng","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_3","first-page":"5533918","article-title":"Transferring CNN with Adaptive Learning for Remote Sensing Scene Classification","volume":"60","author":"Wang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1150","DOI":"10.1109\/JSTARS.2022.3141826","article-title":"GCSANet: A global context spatial attention deep learning network for remote sensing scene classification","volume":"15","author":"Chen","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"De Lima, R.P., and Marfurt, K. (2019). Convolutional neural network for remote-sensing scene classification: Transfer learning analysis. Remote Sens., 12.","DOI":"10.3390\/rs12010086"},{"key":"ref_6","first-page":"4077","article-title":"Prototypical networks for few-shot learning","volume":"30","author":"Snell","year":"2017","journal-title":"Proc. Neural Inf. Process. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2387","DOI":"10.1109\/JSTARS.2021.3052869","article-title":"Research progress on few-shot learning for remote sensing image interpretation","volume":"14","author":"Sun","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_8","first-page":"3379","article-title":"Image block augmentation for one-shot learning","volume":"33","author":"Chen","year":"2019","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_9","first-page":"1410","article-title":"Zero-shot learning with semantic output codes","volume":"22","author":"Palatucci","year":"2009","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhan, T., Song, B., Xu, Y., Wan, M., Wang, X., Yang, G., and Wu, Z. (2021). SSCNN-S: A spectral-spatial convolution neural network with Siamese architecture for change detection. Remote Sens., 13.","DOI":"10.3390\/rs13050895"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Du, L., Li, L., Guo, Y., Wang, Y., Ren, K., and Chen, J. (2021). Two-Stream Deep Fusion Network Based on VAE and CNN for Synthetic Aperture Radar Target Recognition. Remote Sens., 13.","DOI":"10.3390\/rs13204021"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Xu, P., Li, Q., Zhang, B., Wu, F., Zhao, K., Du, X., Yang, C., and Zhong, R. (2021). On-Board Real-Time Ship Detection in HISEA-1 SAR Images Based on CFAR and Lightweight Deep Learning. Remote Sens., 13.","DOI":"10.3390\/rs13101995"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"7918","DOI":"10.1109\/TGRS.2020.3044655","article-title":"Enhanced feature pyramid network with deep semantic embedding for remote sensing scene classification","volume":"59","author":"Wang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1472","DOI":"10.1109\/LGRS.2019.2897652","article-title":"Lifelong learning for scene recognition in remote sensing images","volume":"16","author":"Zhai","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zhao, H., and Li, J. (2021). TRS: Transformers for remote sensing scene classification. Remote Sens., 13.","DOI":"10.3390\/rs13204143"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2030","DOI":"10.1109\/JSTARS.2021.3051569","article-title":"Attention consistent network for remote sensing scene classification","volume":"14","author":"Tang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Huang, W., Yuan, Z., Yang, A., Tang, C., and Luo, X. (2021). TAE-net: Task-adaptive embedding network for few-shot remote sensing scene classification. Remote Sens., 14.","DOI":"10.3390\/rs14010111"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhang, P., Fan, G., Wu, C., Wang, D., and Li, Y. (2021). Task-adaptive embedding learning with dynamic kernel fusion for few-shot remote sensing scene classification. Remote Sens., 13.","DOI":"10.20944\/preprints202108.0389.v1"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"18008","DOI":"10.1109\/JSEN.2022.3195065","article-title":"Few-Shot Unsupervised Specific Emitter Identification Based on Density Peak Clustering Algorithm and Meta-Learning","volume":"22","author":"Xie","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"24980","DOI":"10.1109\/JIOT.2022.3194967","article-title":"Few-Shot Specific Emitter Identification via Deep Metric Ensemble Learning","volume":"9","author":"Wang","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.isprsjprs.2022.07.013","article-title":"Task-specific contrastive learning for few-shot remote sensing image scene classification","volume":"191","author":"Zeng","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","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. (Csur)"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"7844","DOI":"10.1109\/TGRS.2020.3033336","article-title":"DLA-MatchNet for few-shot remote sensing image scene classification","volume":"59","author":"Li","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Li, H., Cui, Z., Zhu, Z., Chen, L., Zhu, J., Huang, H., and Tao, C. (2020). RS-MetaNet: Deep meta metric learning for few-shot remote sensing scene classification. arXiv.","DOI":"10.1109\/TGRS.2020.3027387"},{"key":"ref_25","first-page":"5608011","article-title":"SPNet: Siamese-prototype network for few-shot remote sensing image scene classification","volume":"60","author":"Cheng","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","first-page":"1506905","article-title":"Idln: Iterative distribution learning network for few-shot remote sensing image scene classification","volume":"19","author":"Zeng","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4705611","DOI":"10.1109\/TGRS.2022.3153679","article-title":"MKN: Metakernel networks for few shot remote sensing scene classification","volume":"60","author":"Cui","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"13387","DOI":"10.1109\/TVT.2022.3196103","article-title":"Spatial-Temporal Hybrid Feature Extraction Network for Few-shot Automatic Modulation Classification","volume":"71","author":"Che","year":"2022","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_29","first-page":"5508905","article-title":"Graph neural network via edge convolution for hyperspectral image classification","volume":"19","author":"Hu","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","first-page":"5536716","article-title":"Unsupervised self-correlated learning smoothy enhanced locality preserving graph convolution embedding clustering for hyperspectral images","volume":"60","author":"Ding","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"119508","DOI":"10.1016\/j.eswa.2023.119508","article-title":"Multireceptive field: An adaptive path aggregation graph neural framework for hyperspectral image classification","volume":"217","author":"Zhang","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_32","first-page":"5504205","article-title":"Graph sample and aggregate-attention network for hyperspectral image classification","volume":"19","author":"Ding","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_33","unstructured":"Finn, C., Abbeel, P., and Levine, S. (2017, January 6\u201311). Model-agnostic meta-learning for fast adaptation of deep networks. Proceedings of the International Conference on Machine Learning, Sydney, Australia."},{"key":"ref_34","first-page":"3630","article-title":"Matching networks for one shot learning","volume":"29","author":"Vinyals","year":"2016","journal-title":"Proc. Neural Inf. Process. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P.H., and Hospedales, T.M. (2018, January 18\u201323). Learning to compare: Relation network for few-shot learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00131"},{"key":"ref_36","first-page":"6438","article-title":"Manifold mixup: Better representations by interpolating hidden states","volume":"97","author":"Verma","year":"2019","journal-title":"Int. Conf. Mach. Learn. PMLR"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Yang, Y., and Newsam, S. (2010, January 2\u20135). Bag-of-visual-words and spatial extensions for land-use classification. Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, San Jose, CA, USA.","DOI":"10.1145\/1869790.1869829"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2395","DOI":"10.1080\/01431161.2011.608740","article-title":"High-resolution satellite scene classification using a sparse coding based multiple feature combination","volume":"33","author":"Sheng","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_39","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":"ref_40","unstructured":"Li, Z., Zhou, F., Chen, F., and Li, H. (2017). Meta-sgd: Learning to learn quickly for few-shot learning. arXiv."},{"key":"ref_41","unstructured":"Liu, Y., Lee, J., Park, M., Kim, S., Yang, E., Hwang, S.J., and Yang, Y. (2018). Learning to propagate labels: Transductive propagation network for few-shot learning. arXiv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/3\/831\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:21:33Z","timestamp":1760120493000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/3\/831"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,1]]},"references-count":41,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["rs15030831"],"URL":"https:\/\/doi.org\/10.3390\/rs15030831","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,1]]}}}