{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T17:04:59Z","timestamp":1778346299737,"version":"3.51.4"},"reference-count":74,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T00:00:00Z","timestamp":1695254400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key R&amp;D Program of China","award":["2019YFE0127300"],"award-info":[{"award-number":["2019YFE0127300"]}]},{"name":"the National Key R&amp;D Program of China","award":["30-Y60B01-9003-22\/23"],"award-info":[{"award-number":["30-Y60B01-9003-22\/23"]}]},{"name":"the Major Project of High Resolution Earth Observation System","award":["2019YFE0127300"],"award-info":[{"award-number":["2019YFE0127300"]}]},{"name":"the Major Project of High Resolution Earth Observation System","award":["30-Y60B01-9003-22\/23"],"award-info":[{"award-number":["30-Y60B01-9003-22\/23"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The scope of this research lies in the combination of pre-trained Convolutional Neural Networks (CNNs) and Quantum Convolutional Neural Networks (QCNN) in application to Remote Sensing Image Scene Classification(RSISC). Deep learning (RL) is improving by leaps and bounds pretrained CNNs in Remote Sensing Image (RSI) analysis, and pre-trained CNNs have shown remarkable performance in remote sensing image scene classification (RSISC). Nonetheless, CNNs training require massive, annotated data as samples. When labeled samples are not sufficient, the most common solution is using pre-trained CNNs with a great deal of natural image datasets (e.g., ImageNet). However, these pre-trained CNNs require a large quantity of labelled data for training, which is often not feasible in RSISC, especially when the target RSIs have different imaging mechanisms from RGB natural images. In this paper, we proposed an improved hybrid classical\u2013quantum transfer learning CNNs composed of classical and quantum elements to classify open-source RSI dataset. The classical part of the model is made up of a ResNet network which extracts useful features from RSI datasets. To further refine the network performance, a tensor quantum circuit is subsequently employed by tuning parameters on near-term quantum processors. We tested our models on the open-source RSI dataset. In our comparative study, we have concluded that the hybrid classical\u2013quantum transferring CNN has achieved better performance than other pre-trained CNNs based RSISC methods with small training samples. Moreover, it has been proven that the proposed algorithm improves the classification accuracy while greatly decreasing the amount of model parameters and the sum of training data.<\/jats:p>","DOI":"10.3390\/s23188010","type":"journal-article","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T21:16:49Z","timestamp":1695331009000},"page":"8010","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Remote Sensing Image Scene Classification in Hybrid Classical\u2013Quantum Transferring CNN with Small Samples"],"prefix":"10.3390","volume":"23","author":[{"given":"Zhouwei","family":"Zhang","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"National Engineering Laboratory for Satellite Remote Sensing Applications, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2543-1853","authenticated-orcid":false,"given":"Xiaofei","family":"Mi","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"National Engineering Laboratory for Satellite Remote Sensing Applications, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Yang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"National Engineering Laboratory for Satellite Remote Sensing Applications, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9531-5336","authenticated-orcid":false,"given":"Xiangqin","family":"Wei","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"National Engineering Laboratory for Satellite Remote Sensing Applications, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1963-4145","authenticated-orcid":false,"given":"Yan","family":"Liu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"National Engineering Laboratory for Satellite Remote Sensing Applications, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Yan","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"National Engineering Laboratory for Satellite Remote Sensing Applications, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peizhuo","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingfa","family":"Gu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"National Engineering Laboratory for Satellite Remote Sensing Applications, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Yu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"National Engineering Laboratory for Satellite Remote Sensing Applications, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,21]]},"reference":[{"key":"ref_1","first-page":"154","article-title":"Classification of Remot Sensing Images with Parameterized Quantum Gates","volume":"19","author":"Otgon","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.isprsjprs.2019.04.015","article-title":"Deep Learning in Remote Sensing Applications: A Meta-Analysis and Review","volume":"152","author":"Ma","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_4","first-page":"1","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_5","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":"Chen","year":"2017","journal-title":"Proc. IEEE"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"21","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_7","first-page":"4701813","article-title":"Searching for CNN Architectures for Remote Sensing Scene Classification","volume":"60","author":"Murata","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","unstructured":"Krizhevsky, A., Sutskever, I., and Hintion, G.E. (2012). Ad-vances in Neural Information Processing Systems, Curran Associates."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1242","DOI":"10.1109\/TNNLS.2019.2919608","article-title":"Completely automated CNN architecture design based on blocks","volume":"31","author":"Sun","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_10","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). ImageNet classification with deep convolutional neural networks. Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems (NIPS), Stateline, NV, USA."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., and Fergus, R. (2014, January 6\u201312). Visualizing and understanding convolutional networks. Proceedings of the Computer Vision\u2014ECCV 2014: 13th European Conference, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"ref_12","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 (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Richards, J.A., and Jia, X. (1999). Remote Sensing Digital Image Analysis Image Analysis: An Introduction, Springer.","DOI":"10.1007\/978-3-662-03978-6"},{"key":"ref_15","unstructured":"Coelho, J. (2023, April 20). Solve Any Image Classification Problem Quickly and Easily. Available online: https:\/\/github.com\/pmarcelino\/blog."},{"key":"ref_16","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 Op-portunities","volume":"13","author":"Cheng","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2217","DOI":"10.1109\/JSTARS.2019.2918242","article-title":"Eurosat: A novel dataset and deep learning benchmark for land use and land cover classi-fication","volume":"12","author":"Helber","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1484","DOI":"10.1137\/S0097539795293172","article-title":"Polynomial-time algorithms for prime factorization and discrete logarithms on an quantum computer","volume":"26","author":"Shor","year":"1997","journal-title":"SIAM J. Comput."},{"key":"ref_19","unstructured":"Nielsen, M.A., and Chuang, I.L. (2010). Quantum Computation and Quantum Information, Cambridge University Press."},{"key":"ref_20","first-page":"102382","article-title":"Bulk scanning method of a heavy metal concentration in tailings of a gold mine using SWIR hyperspectral imaging system","volume":"102","author":"Jeong","year":"2021","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1016\/j.scib.2022.02.009","article-title":"Quantum machine learning of eco-environmental surfaces","volume":"67","author":"Yue","year":"2022","journal-title":"Sci. Bull."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Shaik, R.U., Unni, A., and Zeng, W. (2022). Quantum Based Pseudo-Labelling for Hyperspectral Imagery: A Simple and Efficient Semi-Supervised Learning Method for Machine Learning Classifiers. Remote Sens., 14.","DOI":"10.3390\/rs14225774"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Mari, A., Bromley, T.R., and Izaac, J. (2019). Transfer learning in hybrid classical-quantum neural networks. arXiv.","DOI":"10.22331\/q-2020-10-09-340"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Qi, J., and Tejedor, J. (2022, January 22\u201327). Classical-to-Quantum Transfer Learning for Spoken Command Recognition Based on Quantum Neural Networks. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2022, Singapore.","DOI":"10.1109\/ICASSP43922.2022.9747636"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"074001","DOI":"10.1088\/1361-6633\/aab406","article-title":"Machine learning & artificial intelligence in the quantum domain: A review of recent progress","volume":"81","author":"Dunjko","year":"2018","journal-title":"Rep. Prog. Phys."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4213","DOI":"10.1038\/ncomms5213","article-title":"A variational ei-genvalue solver on a photonic quantum processor","volume":"5","author":"Peruzzo","year":"2014","journal-title":"Nat. Commun."},{"key":"ref_27","unstructured":"Farhi, E., Goldstone, J., and Gutmann, S. (2014). A Quantum Approximate Optimization Algorithm. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"140504","DOI":"10.1103\/PhysRevLett.114.140504","article-title":"Experimental Realization of a Quantum Support Vector Machine","volume":"114","author":"Cai","year":"2015","journal-title":"Phys. Rev. Lett."},{"key":"ref_29","unstructured":"Maria, S. (2021). Quantum machine learning models are kernel methods. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1038\/s41586-019-0980-2","article-title":"Supervised learning with quantum-enhanced feature spaces","volume":"567","year":"2019","journal-title":"Nature"},{"key":"ref_31","first-page":"1","article-title":"Power of data in quantum machine learning","volume":"12","author":"Huang","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"235122","DOI":"10.1103\/PhysRevB.102.235122","article-title":"Strategies for solving the Fermi-Hubbard model on near-term quantum computers","volume":"102","author":"Cade","year":"2020","journal-title":"Phys. Rev. B"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., and Socher, R. (2009, January 20\u201325). ImageNet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_34","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_35","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1007\/s42484-020-00012-y","article-title":"Quanvolutional neural networks: Powering image recognition with quantum circuits","volume":"2","author":"Henderson","year":"2020","journal-title":"Quantum Mach. Intell."},{"key":"ref_36","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. (2014). Inception-v4, inception-resnet and the image recognition. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Segedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Yang, C.-H.H., Qi, J., Chen, S.Y.-C., Chen, P., Siniscalchi, S.M., Ma, X., and Lee, C.-H. (2021, January 6\u201311). Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition. Proceedings of the International Conference on Acoustics, Speech and Signal Processing, Toronto, ON, Canada.","DOI":"10.1109\/ICASSP39728.2021.9413453"},{"key":"ref_39","unstructured":"Pointer, I. (2019). Programming PyTorch for Deep Learning: Creating and Deploying Deep Learning, O\u2019Reilly."},{"key":"ref_40","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 classi-fication via learning discriminative cnns","volume":"56","author":"Cheng","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., and Dai, Q. (2021). Simmim: A simple framework for masked image modeling. arXiv.","DOI":"10.1109\/CVPR52688.2022.00943"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"6899","DOI":"10.1109\/TGRS.2018.2845668","article-title":"Remote sensing scene classification using multilayer stacked covariance pooling","volume":"56","author":"He","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Han, X., Zhong, Y., Cao, L., and Zhang, L. (2017). Pre-trained alexnet architecture with pyramid pooling and supervision for high spatial resolution remote sensing image scene classification. Remote Sens., 9.","DOI":"10.3390\/rs9080848"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.neucom.2019.11.068","article-title":"RADC-Net: A residual attention based convolution network for aerial scene classifi-cation","volume":"377","author":"Bi","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/LGRS.2020.2968550","article-title":"Self-Attention-Based Deep Feature Fusion for Remote Sensing Scene Classification","volume":"18","author":"Cao","year":"2021","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"569","DOI":"10.3390\/rs13040569","article-title":"Rotation invariance regularization for remote sensing image scene classification with convolutional neural networks","volume":"13","author":"Kunlun","year":"2021","journal-title":"Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., and Kweon, I.S. (2018, January 8\u201314). Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"8639367","DOI":"10.1155\/2018\/8639367","article-title":"A two-stream deep fusion framework for high-resolution aerial scene classification","volume":"2018","author":"Yu","year":"2018","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_49","first-page":"1","article-title":"Recurrent transformer network for remote sensing scene categorisation","volume":"266","author":"Chen","year":"2018","journal-title":"BMVC"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Zeng, D., Chen, S., Chen, B., and Li, S. (2018). Improving remote sensing scene classification by integrating globalcontext and local-object features. Remote Sens., 10.","DOI":"10.3390\/rs10050734"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Zhang, W., Tang, P., and Zhao, L. (2019). Remote sensing image scene classification using cnn-capsnet. Remote Sens., 11.","DOI":"10.3390\/rs11050494"},{"key":"ref_52","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-Actions Geosci. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1461","DOI":"10.1109\/TNNLS.2019.2920374","article-title":"Skipconnected covariance network for remote sensing scene classification","volume":"31","author":"He","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"8077","DOI":"10.1109\/TGRS.2020.2987060","article-title":"High-resolution remote sensing image scene classification via key filter bank based on con-volutional neural network","volume":"58","author":"Li","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/TGRS.2019.2931801","article-title":"Remote sensing scene classification by gated bidirectional network","volume":"58","author":"Sun","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"5396","DOI":"10.1109\/TIP.2020.2983560","article-title":"Multi-granularity canonical appearance pooling for remote sensing scene classification","volume":"29","author":"Wang","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1926","DOI":"10.1109\/LGRS.2020.3011405","article-title":"Remote sensing image scene classification based on an enhanced attention module","volume":"18","author":"Zhao","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"9768","DOI":"10.1109\/JSTARS.2021.3114404","article-title":"Best representation branch model for remote sensing image scene clas-sification","volume":"14","author":"Zhang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2020.3034752","article-title":"Remote sensing image change detection with transformers","volume":"60","author":"Chen","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_60","first-page":"5606113","article-title":"Gated recurrent multiattention network for vhr remote sensing image classification","volume":"60","author":"Li","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"6549","DOI":"10.1109\/TGRS.2020.3026221","article-title":"Invariant deep compressible covariance pooling for aerial scene categorization","volume":"59","author":"Wang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"9530","DOI":"10.1109\/JSTARS.2021.3109661","article-title":"A multiscale attention network for remote sensing scene images classification","volume":"14","author":"Zhang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_63","first-page":"1","article-title":"When cnns meet vision transformer: A joint framework for remote sensing scene classification","volume":"19","author":"Deng","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"6498","DOI":"10.1109\/TIP.2021.3092816","article-title":"Local semantic enhanced convnet for aerial scene recognition","volume":"30","author":"Bi","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"5751","DOI":"10.1109\/TNNLS.2021.3071369","article-title":"Deep feature aggregation framework driven by graph convolutional network for scene classification in remote sensing","volume":"33","author":"Xu","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"2308","DOI":"10.1109\/TNNLS.2021.3106391","article-title":"Mgml: Multigranularity multilevel feature ensemble network for remote sensing scene classification","volume":"34","author":"Zhao","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Zhao, Q., Ma, Y., Lyu, S., and Chen, L. (2021). Embedded selfdistillation in compact multi-branch ensemble network for remote sensing scene classification. arXiv.","DOI":"10.1109\/TGRS.2021.3126770"},{"key":"ref_68","unstructured":"Ma, O., Lacoste, A., Nieto, X.G.-I., Vazquez, D., and Rodriguez, P. (2021, January 11\u201317). Seasonal contrast: Unsupervised pre-training from uncurated remote sensing data. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada."},{"key":"ref_69","first-page":"5608020","article-title":"An empirical study of remote sensing pretraining","volume":"61","author":"Wang","year":"2022","journal-title":"IEEE Trans. Geo Sci. Remote Sens."},{"key":"ref_70","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 16 \u00d7 16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_71","first-page":"1","article-title":"RingMo: A remote sensing foundation model with masked image modeling","volume":"61","author":"Sun","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11\u201317). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_73","unstructured":"Bergholm, V., Izaac, J., Schuld, M., Gogolin, C., Ahmed, S., Ajith, V., Alam, M.S., Alonso-Linaje, G., Narayanan, B.A., and Asadi, A. (2018). PennyLane: Automatic differentiation of hybrid quantum-classical computations. arXiv."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1038\/s43588-022-00311-3","article-title":"Challenges and opportunities in quantum machine learning","volume":"2","author":"Cerezo","year":"2022","journal-title":"Nat. Comput. Sci."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/18\/8010\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:55:09Z","timestamp":1760129709000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/18\/8010"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,21]]},"references-count":74,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["s23188010"],"URL":"https:\/\/doi.org\/10.3390\/s23188010","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,21]]}}}