{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T20:37:28Z","timestamp":1770237448223,"version":"3.49.0"},"reference-count":53,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,29]],"date-time":"2023-01-29T00:00:00Z","timestamp":1674950400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42006164"],"award-info":[{"award-number":["42006164"]}]},{"name":"National Natural Science Foundation of China","award":["U2006207"],"award-info":[{"award-number":["U2006207"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With deep learning-based methods growing (even with scarce data in some fields), few-shot remote sensing scene classification (FSRSSC) has received a lot of attention. One mainstream approach uses base data to train a feature extractor (FE) in the pre-training phase and employs novel data to design the classifier and complete the classification task in the meta-test phase. Due to the scarcity of remote sensing data, obtaining a suitable feature extractor for remote sensing data and designing a robust classifier have become two major challenges. In this paper, we propose a novel dictionary learning (DL) algorithm for few-shot remote sensing scene classification to address these two difficulties. First, we use natural image datasets with sufficient data to obtain a pre-trained feature extractor. We fine-tune the parameters with the remote sensing dataset to make the feature extractor suitable for remote sensing data. Second, we design the kernel space classifier to map the features to a high-dimensional space and embed the label information into the dictionary learning to improve the discrimination of features for classification. Extensive experiments on four popular remote sensing scene classification datasets demonstrate the effectiveness of our proposed dictionary learning method.<\/jats:p>","DOI":"10.3390\/rs15030773","type":"journal-article","created":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T10:19:28Z","timestamp":1675073968000},"page":"773","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Dictionary Learning for Few-Shot Remote Sensing Scene Classification"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4223-9031","authenticated-orcid":false,"given":"Yuteng","family":"Ma","sequence":"first","affiliation":[{"name":"First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China"},{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"},{"name":"Technology Innovation Center for Ocean Telemetry, Ministry of Natural Resources, Qingdao 266061, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3358-8245","authenticated-orcid":false,"given":"Junmin","family":"Meng","sequence":"additional","affiliation":[{"name":"First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China"},{"name":"Technology Innovation Center for Ocean Telemetry, Ministry of Natural Resources, Qingdao 266061, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1408-5514","authenticated-orcid":false,"given":"Baodi","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Control Science and Engineering, China University of Petroleum (East China), Qingdao 266580, China"}]},{"given":"Lina","family":"Sun","sequence":"additional","affiliation":[{"name":"First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China"},{"name":"Technology Innovation Center for Ocean Telemetry, Ministry of Natural Resources, Qingdao 266061, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1928-8556","authenticated-orcid":false,"given":"Hao","family":"Zhang","sequence":"additional","affiliation":[{"name":"First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China"},{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"},{"name":"Technology Innovation Center for Ocean Telemetry, Ministry of Natural Resources, Qingdao 266061, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3949-985X","authenticated-orcid":false,"given":"Peng","family":"Ren","sequence":"additional","affiliation":[{"name":"Technology Innovation Center for Ocean Telemetry, Ministry of Natural Resources, Qingdao 266061, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Johnson, B.A., and Jozdani, S.E. (2019). Local Climate Zone (LCZ) Map Accuracy Assessments Should Account for Land Cover Physical Characteristics that Affect the Local Thermal Environment. Remote Sens., 11.","DOI":"10.3390\/rs11202420"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.landurbplan.2010.12.009","article-title":"A case study on the relation between city planning and urban growth using remote sensing and spatial metrics","volume":"100","author":"Pham","year":"2011","journal-title":"Landsc. Urban Plan."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1109\/LGRS.2015.2513443","article-title":"Bag-of-Visual-Words Scene Classifier with Local and Global Features for High Spatial Resolution Remote Sensing Imagery","volume":"13","author":"Zhu","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Manfreda, S., McCabe, M.F., Miller, P.E., Lucas, R., Pajuelo Madrigal, V., Mallinis, G., Ben Dor, E., Helman, D., Estes, L., and Ciraolo, G. (2018). On the Use of Unmanned Aerial Systems for Environmental Monitoring. Remote Sens., 10.","DOI":"10.20944\/preprints201803.0097.v1"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_6","first-page":"5511812","article-title":"Semi-supervised locality preserving dense graph neural network with ARMA filters and context-aware learning for hyperspectral image classification","volume":"60","author":"Ding","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4561","DOI":"10.1109\/JSTARS.2021.3074469","article-title":"Multiscale graph sample and aggregate network with context-aware learning for hyperspectral image classification","volume":"14","author":"Ding","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_8","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_9","doi-asserted-by":"crossref","unstructured":"Ding, Y., Zhang, Z., Zhao, X., Cai, W., He, F., Cai, Y., and Cai, W. (2022). Deep hybrid: Multi-Graph neural network collaboration for hyperspectral image classification. Def. Technol., in press.","DOI":"10.1016\/j.neucom.2022.06.031"},{"key":"ref_10","unstructured":"Snell, J., Swersky, K., and Zemel, R.S. (2017). Prototypical Networks for Few-shot Learning. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5151","DOI":"10.1109\/TCSVT.2021.3135023","article-title":"MDFM: Multi-Decision Fusing Model for Few-Shot Learning","volume":"32","author":"Shao","year":"2021","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wang, Y., Xu, C., Liu, C., Zhang, L., and Fu, Y. (2020, January 13\u201319). Instance Credibility Inference for Few-Shot Learning. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01285"},{"key":"ref_13","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of oriented gradients for human detection. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive Image Features from Scale-Invariant Keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1109\/TPAMI.2002.1017623","article-title":"Multiresolution gray-scale and rotation invariant texture classification with local binary patterns","volume":"24","author":"Ojala","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","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. Association for Computing Machinery, San Jose, CA, USA.","DOI":"10.1145\/1869790.1869829"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.neucom.2022.06.031","article-title":"Multi-feature Fusion: Graph Neural Network and CNN Combining for Hyperspectral Image Classification","volume":"501","author":"Ding","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.ins.2022.04.006","article-title":"AF2GNN: Graph convolution with adaptive filters and aggregator fusion for hyperspectral image classification","volume":"602","author":"Ding","year":"2022","journal-title":"Inf. Sci."},{"key":"ref_19","first-page":"5536016","article-title":"Self-supervised locality preserving low-pass graph convolutional embedding for large-scale hyperspectral image clustering","volume":"60","author":"Ding","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","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_21","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 ICML International Conference on Machine Learning, Sydney, Australia."},{"key":"ref_22","unstructured":"Nichol, A., Achiam, J., and Schulman, J. (2018). On First-Order Meta-Learning Algorithms. arXiv."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","unstructured":"Alajaji, D.A., and Alhichri, H. (2020, January 4\u20135). Few Shot Scene Classification in Remote Sensing using Meta-Agnostic Machine. Proceedings of the 2020 6th Conference on Data Science and Machine Learning Applications (CDMA), Riyadh, Saudi Arabia.","DOI":"10.1109\/CDMA47397.2020.00019"},{"key":"ref_25","unstructured":"Koch, G., Zemel, R., and Salakhutdinov, R. (2015, January 6\u201311). Siamese neural networks for one-shot image recognition. Proceedings of the 32nd International Conference on Machine Learning, Lille, France."},{"key":"ref_26","first-page":"3630","article-title":"Matching networks for one shot learning","volume":"29","author":"Vinyals","year":"2016","journal-title":"NeurIPS"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P.H., and Hospedales, T.M. (2018, January 18\u201322). Learning to compare: Relation network for few-shot learning. Proceedings of the CVPR IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00131"},{"key":"ref_28","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":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Alajaji, D., Alhichri, H.S., Ammour, N., and Alajlan, N. (2020, January 9\u201311). Few-shot learning for remote sensing scene classification. Proceedings of the M2GARSS, 2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS), Tunis, Tunisia.","DOI":"10.1109\/M2GARSS47143.2020.9105154"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhang, P., Bai, Y., Wang, D., Bai, B., and Li, Y. (2021). Few-Shot Classification of Aerial Scene Images via Meta-Learning. Remote Sens., 13.","DOI":"10.20944\/preprints202010.0033.v1"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Dvornik, N., Schmid, C., and Mairal, J. (2019). Diversity with Cooperation: Ensemble Methods for Few-Shot Classification. arXiv.","DOI":"10.1109\/ICCV.2019.00382"},{"key":"ref_32","unstructured":"Yue, Z., Zhang, H., Sun, Q., and Hua, X.S. (2020). Interventional Few-Shot Learning. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Shao, S., Xing, L., Wang, Y., Xu, R., Zhao, C., Wang, Y., and Liu, B. (2021, January 20\u201324). MHFC: Multi-Head Feature Collaboration for Few-Shot Learning. Proceedings of the ACM MM, 29th ACM International Conference on Multimedia, Virtual Event, China.","DOI":"10.1145\/3474085.3475553"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lee, K., Maji, S., Ravichandran, A., and Soatto, S. (2019, January 15\u201320). Meta-learning with differentiable convex optimization. Proceedings of the CVPR 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01091"},{"key":"ref_35","unstructured":"Ren, M., Ravi, S., Triantafillou, E., Snell, J., Swersky, K., Tenenbaum, J.B., Larochelle, H., and Zemel, R.S. (May, January 30). Meta-Learning for Semi-Supervised Few-Shot Classification. Proceedings of the International Conference on Learning Representations, Vancouver, BC, Canada."},{"key":"ref_36","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_37","doi-asserted-by":"crossref","first-page":"2486","DOI":"10.1109\/TGRS.2016.2645610","article-title":"Accurate Object Localization in Remote Sensing Images Based on Convolutional Neural Networks","volume":"55","author":"Long","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Xiao, Z., Long, Y., Li, D., Wei, C., Tang, G., and Liu, J. (2017). High-Resolution Remote Sensing Image Retrieval Based on CNNs from a Dimensional Perspective. Remote Sens., 9.","DOI":"10.3390\/rs9070725"},{"key":"ref_39","unstructured":"Xia, G.S., Yang, W., Delon, J., Gousseau, Y., Sun, H., and Maitre, H. (2010, January 5\u20137). Structural high-resolution satellite image indexing. Proceedings of the ISPRS TC VII Symposium\u2014100 Years ISPRS, Vienna, Austria."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1109\/LGRS.2010.2055033","article-title":"Satellite Image Classification via Two-Layer Sparse Coding With Biased Image Representation","volume":"8","author":"Dai","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","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_42","unstructured":"Li, Z., Zhou, F., Chen, F., and Li, H. (2017). Meta-sgd: Learning to learn quickly for few-shot learning. arXiv."},{"key":"ref_43","unstructured":"Oreshkin, B.N., Rodriguez, P., and Lacoste, A. (2018, January 2\u20138). TADAM: Task dependent adaptive metric for improved few-shot learning. Proceedings of the NeurIPS, 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montreal, QC, Canada."},{"key":"ref_44","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_45","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_46","doi-asserted-by":"crossref","unstructured":"Simon, C., Koniusz, P., Nock, R., and Harandi, M. (2020, January 13\u201319). Adaptive subspaces for few-shot learning. Proceedings of the CVPR, 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00419"},{"key":"ref_47","unstructured":"Liu, Y., Lee, J., Park, M., Kim, S., Yang, E., Hwang, S.J., and Yang, J. (2018). Learning to propagate labels: Transductive propagation network for few-shot learning. arXiv."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zhang, P., Li, Y., Wang, D., and Wang, J. (2021). RS-SSKD: Self-Supervision Equipped with Knowledge Distillation for Few-Shot Remote Sensing Scene Classification. Sensors, 21.","DOI":"10.3390\/s21051566"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Ye, H.J., Hu, H., Zhan, D.C., and Sha, F. (2020, January 14\u201319). Few-shot learning via embedding adaptation with set-to-set functions. Proceedings of the CVPR, 2020 Conference on Computer Vision and Pattern Recognition, Virtual.","DOI":"10.1109\/CVPR42600.2020.00883"},{"key":"ref_50","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_51","first-page":"1097","article-title":"Multi-attention DeepEMD for Few-Shot Learning in Remote Sensing","volume":"Volume 9","author":"Yuan","year":"2020","journal-title":"Proceedings of the ITAIC, 2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC)"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Zhang, C., Cai, Y., Lin, G., and Shen, C. (2020, January 13\u201319). DeepEMD: Few-Shot Image Classification with Differentiable Earth Mover\u2019s Distance and Structured Classifiers. Proceedings of the CVPR, 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01222"},{"key":"ref_53","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"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/3\/773\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:19:09Z","timestamp":1760120349000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/3\/773"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,29]]},"references-count":53,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["rs15030773"],"URL":"https:\/\/doi.org\/10.3390\/rs15030773","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,29]]}}}