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Scene classification in many practical cases faces the challenge of few-shot conditions. The major difficulty of few-shot remote sensing image scene classification is how to extract effective features from insufficient labeled data. To solve these issues, a multi-scale graph-based feature fusion (MGFF) model is proposed for few-shot remote sensing image scene classification. In the MGFF model, a graph-based feature construction model is developed to transform traditional image features into graph-based features, which aims to effectively represent the spatial relations among images. Then, a graph-based feature fusion model is proposed to integrate graph-based features of multiple scales, which aims to enhance sample discrimination based on different scale information. Experimental results on two public remote sensing datasets prove that the MGFF model can achieve superior accuracy than other few-shot scene classification approaches.<\/jats:p>","DOI":"10.3390\/rs14215550","type":"journal-article","created":{"date-parts":[[2022,11,4]],"date-time":"2022-11-04T04:00:51Z","timestamp":1667534451000},"page":"5550","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Multi-Scale Graph-Based Feature Fusion for Few-Shot Remote Sensing Image Scene Classification"],"prefix":"10.3390","volume":"14","author":[{"given":"Nan","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Electronics and Information, Northwestern Polytechnical University, Xi\u2019an 710072, China"}]},{"given":"Haowen","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Electronics and Information, Northwestern Polytechnical University, Xi\u2019an 710072, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4858-823X","authenticated-orcid":false,"given":"Jie","family":"Geng","sequence":"additional","affiliation":[{"name":"School of Electronics and Information, Northwestern Polytechnical University, Xi\u2019an 710072, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3660","DOI":"10.1109\/TGRS.2016.2523563","article-title":"Semantic annotation of high-resolution satellite images via weakly supervised learning","volume":"54","author":"Yao","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","first-page":"1","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_3","doi-asserted-by":"crossref","first-page":"2108","DOI":"10.1109\/TGRS.2015.2496185","article-title":"Dirichlet-derived multiple topic scene classification model for high spatial resolution remote sensing imagery","volume":"54","author":"Zhao","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.isprsjprs.2019.04.010","article-title":"Investigating the effects of 3D urban morphology on the surface urban heat island effect in urban functional zones by using high-resolution remote sensing data: A case study of Wuhan, Central China","volume":"152","author":"Huang","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5969","DOI":"10.1109\/TIP.2021.3089936","article-title":"Speckle-free SAR image ship detection","volume":"30","author":"Chen","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3965","DOI":"10.1109\/TGRS.2017.2685945","article-title":"AID: A benchmark data set for performance evaluation of aerial scene classification","volume":"55","author":"Xia","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1023\/A:1011139631724","article-title":"Modeling the shape of the scene: A holistic representation of the spatial envelope","volume":"42","author":"Oliva","year":"2001","journal-title":"Int. J. Comput. Vis."},{"key":"ref_9","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, Beijing, China.","DOI":"10.1145\/1869790.1869829"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"8588","DOI":"10.1080\/01431161.2013.845925","article-title":"Extreme value theory-based calibration for the fusion of multiple features in high-resolution satellite scene classification","volume":"34","author":"Shao","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Negrel, R., Picard, D., and Gosselin, P.H. (2014, January 18\u201320). Evaluation of second-order visual features for land-use classification. Proceedings of the 2014 12th International Workshop on Content-Based Multimedia Indexing (CBMI), Klagenfurt, Austria.","DOI":"10.1109\/CBMI.2014.6849835"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2296","DOI":"10.1080\/01431161.2014.890762","article-title":"A 2-D wavelet decomposition-based bag-of-visual-words model for land-use scene classification","volume":"35","author":"Zhao","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst., 25."},{"key":"ref_14","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Penatti, O.A., Nogueira, K., and Dos Santos, J.A. (2015, January 7\u201312). Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Boston, MA, USA.","DOI":"10.1109\/CVPRW.2015.7301382"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1109\/LGRS.2018.2799877","article-title":"PolSAR image classification using polarimetric-feature-driven deep convolutional neural network","volume":"15","author":"Chen","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"165356","DOI":"10.1016\/j.ijleo.2020.165356","article-title":"Remote sensing image scene classification using CNN-MLP with data augmentation","volume":"221","author":"Shawky","year":"2020","journal-title":"Optik"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"113067","DOI":"10.1016\/j.eswa.2019.113067","article-title":"Acoustic scene classification using deep CNN with fine-resolution feature","volume":"143","author":"Zhang","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.neucom.2021.01.085","article-title":"Image scene geometry recognition using low-level features fusion at multi-layer deep CNN","volume":"440","author":"Khan","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_21","first-page":"102279","article-title":"JellyNet: The convolutional neural network jellyfish bloom detector","volume":"97","author":"Mcilwaine","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_22","unstructured":"Gidaris, S., Bursuc, A., Komodakis, N., P\u00e9rez, P., and Cord, M. (November, January 27). Boosting few-shot visual learning with self-supervision. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Chu, W.H., Li, Y.J., Chang, J.C., and Wang, Y.C.F. (2019, January 15\u201320). Spot and learn: A maximum-entropy patch sampler for few-shot image classification. Proceedings of the IEEE\/CVF conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00641"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4238","DOI":"10.1109\/TGRS.2015.2393857","article-title":"Effective and efficient midlevel visual elements-oriented land-use classification using VHR remote sensing images","volume":"53","author":"Cheng","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"6641","DOI":"10.1109\/TIP.2020.2992883","article-title":"SAR image speckle filtering with context covariance matrix formulation and similarity test","volume":"29","author":"Chen","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_26","first-page":"1","article-title":"Auto-AD: Autonomous hyperspectral anomaly detection network based on fully convolutional autoencoder","volume":"60","author":"Wang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","first-page":"1","article-title":"Attention-based multiscale residual adaptation network for cross-scene classification","volume":"60","author":"Zhu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","first-page":"1","article-title":"A lightweight and robust lie group-convolutional neural networks joint representation for remote sensing scene classification","volume":"60","author":"Xu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","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_30","doi-asserted-by":"crossref","first-page":"5148","DOI":"10.1109\/TGRS.2017.2702596","article-title":"Remote sensing scene classification by unsupervised representation learning","volume":"55","author":"Lu","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","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_32","doi-asserted-by":"crossref","first-page":"2504","DOI":"10.1109\/TGRS.2019.2951779","article-title":"Multisource compensation network for remote sensing cross-domain scene classification","volume":"58","author":"Lu","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1016\/j.patcog.2016.07.001","article-title":"Towards better exploiting convolutional neural networks for remote sensing scene classification","volume":"61","author":"Nogueira","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_34","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_35","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.1109\/TGRS.2018.2864987","article-title":"Scene classification with recurrent attention of VHR remote sensing images","volume":"57","author":"Wang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","first-page":"1","article-title":"Class-Level Prototype Guided Multiscale Feature Learning for Remote Sensing Scene Classification With Limited Labels","volume":"60","author":"Tang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Pires de Lima, R., and Marfurt, K. (2019). Convolutional neural network for remote-sensing scene classification: Transfer learning analysis. Remote Sens., 12.","DOI":"10.3390\/rs12010086"},{"key":"ref_38","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_39","first-page":"3637","article-title":"Matching networks for one shot learning","volume":"29","author":"Vinyals","year":"2016","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_40","first-page":"4080","article-title":"Prototypical networks for few-shot learning","volume":"30","author":"Snell","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_41","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_42","doi-asserted-by":"crossref","unstructured":"Zhang, X., Qiang, Y., Sung, F., Yang, Y., and Hospedales, T. (2020, January 19\u201324). RelationNet2: Deep comparison network for few-shot learning. Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK.","DOI":"10.1109\/IJCNN48605.2020.9206909"},{"key":"ref_43","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 2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS), Tunis, Tunisia.","DOI":"10.1109\/M2GARSS47143.2020.9105154"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1091","DOI":"10.1109\/TCSVT.2020.2995754","article-title":"Multi-scale metric learning for few-shot learning","volume":"31","author":"Jiang","year":"2020","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_45","first-page":"1","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_46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2022.3224452","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_47","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_48","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_49","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_50","unstructured":"Koch, G., Zemel, R., and Salakhutdinov, R. (2015, January 6\u201311). Siamese neural networks for one-shot image recognition. Proceedings of the ICML Deep Learning Workshop, Lille, France."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Kim, J., Kim, T., Kim, S., and Yoo, C.D. (2019, January 16\u201317). Edge-labeling graph neural network for few-shot learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00010"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Gidaris, S., and Komodakis, N. (2019, January 15\u201320). Generating classification weights with gnn denoising autoencoders for few-shot learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00011"},{"key":"ref_53","unstructured":"Hamilton, W.L., Ying, R., and Leskovec, J. (2017). Representation learning on graphs: Methods and applications. arXiv."},{"key":"ref_54","unstructured":"Kipf, T.N., and Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv."},{"key":"ref_55","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y. (2017). Graph attention networks. arXiv."},{"key":"ref_56","unstructured":"Wu, F., Souza, A., Zhang, T., Fifty, C., Yu, T., and Weinberger, K. (2019, January 9\u201315). Simplifying graph convolutional networks. Proceedings of the International Conference on Machine Learning, PMLR, Long Beach, CA, USA."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Yuan, Z., Huang, W., Tang, C., Yang, A., and Luo, X. (2022). Graph-Based Embedding Smoothing Network for Few-Shot Scene Classification of Remote Sensing Images. Remote Sens., 14.","DOI":"10.3390\/rs14051161"},{"key":"ref_58","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_59","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_60","first-page":"1","article-title":"SCL-MLNet: Boosting Few-Shot Remote Sensing Scene Classification via Self-Supervised Contrastive Learning","volume":"60","author":"Li","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_61","unstructured":"Li, Z., Zhou, F., Chen, F., and Li, H. (2017). Meta-sgd: Learning to learn quickly for few-shot learning. arXiv."},{"key":"ref_62","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."},{"key":"ref_63","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, PMLR, Sydney, Australia."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/21\/5550\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:09:58Z","timestamp":1760144998000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/21\/5550"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,3]]},"references-count":63,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["rs14215550"],"URL":"https:\/\/doi.org\/10.3390\/rs14215550","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,3]]}}}