{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T20:24:39Z","timestamp":1783628679337,"version":"3.55.0"},"reference-count":69,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,2,26]],"date-time":"2022-02-26T00:00:00Z","timestamp":1645833600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41871226"],"award-info":[{"award-number":["41871226"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>As a fundamental task in the field of remote sensing, scene classification is increasingly attracting attention. The most popular way to solve scene classification is to train a deep neural network with a large-scale remote sensing dataset. However, given a small amount of data, how to train a deep neural network with outstanding performance remains a challenge. Existing methods seek to take advantage of transfer knowledge or meta-knowledge to resolve the scene classification issue of remote sensing images with a handful of labeled samples while ignoring various class-irrelevant noises existing in scene features and the specificity of different tasks. For this reason, in this paper, an end-to-end graph neural network is presented to enhance the performance of scene classification in few-shot scenarios, referred to as the graph-based embedding smoothing network (GES-Net). Specifically, GES-Net adopts an unsupervised non-parametric regularizer, called embedding smoothing, to regularize embedding features. Embedding smoothing can capture high-order feature interactions in an unsupervised manner, which is adopted to remove undesired noises from embedding features and yields smoother embedding features. Moreover, instead of the traditional sample-level relation representation, GES-Net introduces a new task-level relation representation to construct the graph. The task-level relation representation can capture the relations between nodes from the perspective of the whole task rather than only between samples, which can highlight subtle differences between nodes and enhance the discrimination of the relations between nodes. Experimental results on three public remote sensing datasets, UC Merced, WHU-RS19, and NWPU-RESISC45, showed that the proposed GES-Net approach obtained state-of-the-art results in the settings of limited labeled samples.<\/jats:p>","DOI":"10.3390\/rs14051161","type":"journal-article","created":{"date-parts":[[2022,2,27]],"date-time":"2022-02-27T20:48:33Z","timestamp":1645994913000},"page":"1161","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Graph-Based Embedding Smoothing Network for Few-Shot Scene Classification of Remote Sensing Images"],"prefix":"10.3390","volume":"14","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"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"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"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3839-9267","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"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6159-7395","authenticated-orcid":false,"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"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5688-0324","authenticated-orcid":false,"given":"Xiaobo","family":"Luo","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"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,26]]},"reference":[{"key":"ref_1","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_2","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_3","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_4","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.isprsjprs.2016.01.004","article-title":"Learning multiscale and deep representations for classifying remotely sensed imagery","volume":"113","author":"Zhao","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1490","DOI":"10.1002\/ldr.3337","article-title":"Monitoring ecosystem service change in the City of Shenzhen by the use of high-resolution remotely sensed imagery and deep learning","volume":"30","author":"Huang","year":"2019","journal-title":"Land Degrad. Dev."},{"key":"ref_6","first-page":"6180","article-title":"Adaptive deep sparse semantic modeling framework for high spatial resolution image scene classification","volume":"56","author":"Zhu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2270","DOI":"10.1109\/JSTARS.2016.2542193","article-title":"Parallel and distributed dimensionality reduction of hyperspectral data on cloud computing architectures","volume":"9","author":"Wu","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Chen, J., Wang, C., Ma, Z., Chen, J., He, D., and Ackland, S. (2018). Remote sensing scene classification based on convolutional neural networks pre-trained using attention-guided sparse filters. Remote Sens., 10.","DOI":"10.3390\/rs10020290"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2689","DOI":"10.1109\/TGRS.2017.2781712","article-title":"Scene classification based on the sparse homogeneous\u2013heterogeneous topic feature model","volume":"56","author":"Zhu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"7109","DOI":"10.1109\/TGRS.2018.2848473","article-title":"Scene classification based on multiscale convolutional neural network","volume":"56","author":"Liu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1436","DOI":"10.1109\/LGRS.2017.2691013","article-title":"Transfer learning with fully pretrained deep convolution networks for land-use classification","volume":"14","author":"Zhao","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","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 2019 IEEE\/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00815"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chu, W.H., Li, Y.J., Chang, J.C., and Wang, Y.C.F. (2019, January 16\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_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","unstructured":"Garcia, V., and Bruna, J. (May, January 30). Few-shot learning with graph neural networks. Proceedings of the 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Bartlett, P., and Shawe-Taylor, J. (1999). Generalization performance of support vector machines and other pattern classifiers. Advances in Kernel Methods: Support Vector Learning, MIT Press.","DOI":"10.7551\/mitpress\/1130.003.0007"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1040","DOI":"10.1162\/neco.1995.7.5.1040","article-title":"Lower bounds on the VC dimension of smoothly parameterized function classes","volume":"7","author":"Lee","year":"1995","journal-title":"Neural Comput."},{"key":"ref_18","unstructured":"Verma, V., Lamb, A., Beckham, C., Najafi, A., Mitliagkas, I., Lopez-Paz, D., and Bengio, Y. (2019, January 9\u201315). Manifold mixup: Better representations by interpolating hidden states. Proceedings of the 36th International Conference on Machine Learning (PMLR), Long Beach, CA, USA."},{"key":"ref_19","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_20","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_21","doi-asserted-by":"crossref","first-page":"2876","DOI":"10.1109\/TIP.2021.3055632","article-title":"A Self-Training Approach for Point-Supervised Object Detection and Counting in Crowds","volume":"30","author":"Wang","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhu, S., Du, B., Zhang, L., and Li, X. (2021). Attention-Based Multiscale Residual Adaptation Network for Cross-Scene Classification. IEEE Trans. Geosci. Remote Sens., 60.","DOI":"10.1109\/TGRS.2021.3056624"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Chen, J., Qiu, X., Ding, C., and Wu, Y. (2021). CVCMFF Net: Complex-Valued Convolutional and Multifeature Fusion Network for Building Semantic Segmentation of InSAR Images. IEEE Trans. Geosci. Remote Sens., 60.","DOI":"10.1109\/TGRS.2021.3068124"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Xu, C., Zhu, G., and Shu, J. (2021). A Lightweight and Robust Lie Group-Convolutional Neural Networks Joint Representation for Remote Sensing Scene Classification. IEEE Trans. Geosci. Remote Sens., 60.","DOI":"10.1109\/TGRS.2020.3048024"},{"key":"ref_25","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_26","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 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Boston, MA, USA.","DOI":"10.1109\/CVPRW.2015.7301382"},{"key":"ref_27","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_28","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_29","doi-asserted-by":"crossref","first-page":"7894","DOI":"10.1109\/TGRS.2019.2917161","article-title":"A feature aggregation convolutional neural network for remote sensing scene classification","volume":"57","author":"Lu","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","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_31","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_32","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_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 PMLR, Sydney, Australia."},{"key":"ref_34","unstructured":"Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., and Lillicrap, T. (2016, January 19\u201324). Meta-learning with memory-augmented neural networks. Proceedings of the International Conference on Machine Learning PMLR, New York, NY, USA."},{"key":"ref_35","unstructured":"Tokmakov, P., Wang, Y.X., and Hebert, M. (November, January 27). Learning compositional representations for few-shot recognition. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea."},{"key":"ref_36","unstructured":"Li, H., Dong, W., Mei, X., Ma, C., Huang, F., and Hu, B.G. (2019, January 9\u201315). Lgm-net: Learning to generate matching networks for few-shot learning. Proceedings of the International Conference on Machine Learning PMLR, Long Beach, CA, USA."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2290","DOI":"10.1109\/TGRS.2018.2872830","article-title":"Deep few-shot learning for hyperspectral image classification","volume":"57","author":"Liu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Gao, K., Liu, B., Yu, X., Qin, J., Zhang, P., and Tan, X. (2020). Deep relation network for hyperspectral image few-shot classification. Remote Sens., 12.","DOI":"10.3390\/rs12060923"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Rostami, M., Kolouri, S., Eaton, E., and Kim, K. (2019). Deep transfer learning for few-shot sar image classification. Remote Sens., 11.","DOI":"10.20944\/preprints201905.0030.v1"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"6983","DOI":"10.1109\/TGRS.2020.3027387","article-title":"RS-MetaNet: Deep Metametric Learning for Few-Shot Remote Sensing Scene Classification","volume":"59","author":"Li","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","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_42","unstructured":"Vedaldi, A., Bischof, H., Brox, T., and Frahm, J.M. (2020). A broader study of cross-domain few-shot learning. European Conference on Computer Vision, Springer."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"10035","DOI":"10.1109\/TGRS.2020.3034344","article-title":"Cross-Domain Scene Classification by Integrating Multiple Incomplete Sources","volume":"59","author":"Gong","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","first-page":"3320","article-title":"How transferable are features in deep neural networks?","volume":"27","author":"Yosinski","year":"2014","journal-title":"Proc. Adv. Neural Inf. Process. Syst."},{"key":"ref_45","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the International Conference on Machine Learning PMLR, Lille, France."},{"key":"ref_46","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Tompson, J., Goroshin, R., Jain, A., LeCun, Y., and Bregler, C. (2015, January 7\u201312). Efficient object localization using convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298664"},{"key":"ref_48","unstructured":"Rodr\u00edguez, P., Gonzalez, J., Cucurull, G., Gonfaus, J.M., and Roca, X. (2016). Regularizing cnns with locally constrained decorrelations. arXiv."},{"key":"ref_49","first-page":"901","article-title":"Weight normalization: A simple reparameterization to accelerate training of deep neural networks","volume":"29","author":"Salimans","year":"2016","journal-title":"Proc. Adv. Neural Inf. Process. Syst."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., and Lopez-Paz, D. (2017). mixup: Beyond empirical risk minimization. arXiv.","DOI":"10.1007\/978-1-4899-7687-1_79"},{"key":"ref_51","first-page":"2399","article-title":"Manifold regularization: A geometric framework for learning from labeled and unlabeled examples","volume":"7","author":"Belkin","year":"2006","journal-title":"J. Mach. Learn. Res."},{"key":"ref_52","unstructured":"Cho, K., and Zhao, J. (2019, January 16\u201320). Retrieval-augmented convolutional neural networks against adversarial examples. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1109\/72.788640","article-title":"An overview of statistical learning theory","volume":"10","author":"Vapnik","year":"1999","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Iscen, A., Tolias, G., Avrithis, Y., and Chum, O. (2019, January 16\u201320). Label propagation for deep semi-supervised learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00521"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Liu, B., Wu, Z., Hu, H., and Lin, S. (2019, January 27\u201328). Deep metric transfer for label propagation with limited annotated data. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision Workshops, ICCV Workshops 2019, Seoul, Korea.","DOI":"10.1109\/ICCVW.2019.00167"},{"key":"ref_56","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_57","unstructured":"Snell, J., Swersky, K., and Zemel, R. (2017, January 4\u20139). Prototypical networks for few-shot learning. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_58","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 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00131"},{"key":"ref_59","unstructured":"Glorot, X., Bordes, A., and Bengio, Y. (2011, January 11\u201313). Deep sparse rectifier neural networks. Proceedings of the International Conference Artificial Intelligence and Statistics, JMLR Workshop and Conference Proceedings, Fort Lauderdale, FL, USA."},{"key":"ref_60","unstructured":"Zhou, D., Bousquet, O., Lal, T.N., Weston, J., and Sch\u00f6lkopf, B. (2004, January 13\u201318). Learning with local and global consistency. Proceedings of the Advances in Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_61","unstructured":"Chung, F.R., and Graham, F.C. (1997). Spectral Graph Theory, AMS, American Mathematical Society. Number 92."},{"key":"ref_62","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 ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL GIS 2010), San Jose, CA, USA.","DOI":"10.1145\/1869790.1869829"},{"key":"ref_63","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_64","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_65","unstructured":"Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A. (2017, January 4\u20139). Automatic differentiation in pytorch. Proceedings of the Workshop Advances Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_66","unstructured":"Kingma, D.P., and Ba, J. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the 3rd International Conference for Learning Representations, San Diego, CA, USA."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 21\u201326). Deep residual learning for image recognition. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_68","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_69","unstructured":"Li, Z., Zhou, F., Chen, F., and Li, H. (2017). Meta-sgd: Learning to learn quickly for few-shot learning. arXiv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/5\/1161\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:28:09Z","timestamp":1760135289000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/5\/1161"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,26]]},"references-count":69,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["rs14051161"],"URL":"https:\/\/doi.org\/10.3390\/rs14051161","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,26]]}}}