{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T01:08:02Z","timestamp":1780621682611,"version":"3.54.1"},"reference-count":54,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,12]],"date-time":"2022-10-12T00:00:00Z","timestamp":1665532800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China Major Program","award":["42192580"],"award-info":[{"award-number":["42192580"]}]},{"name":"National Natural Science Foundation of China Major Program","award":["42192584"],"award-info":[{"award-number":["42192584"]}]},{"name":"National Natural Science Foundation of China Major Program","award":["62201063"],"award-info":[{"award-number":["62201063"]}]},{"name":"National Natural Science Foundation of China Major Program","award":["4214065"],"award-info":[{"award-number":["4214065"]}]},{"name":"National Natural Science Foundation of China","award":["42192580"],"award-info":[{"award-number":["42192580"]}]},{"name":"National Natural Science Foundation of China","award":["42192584"],"award-info":[{"award-number":["42192584"]}]},{"name":"National Natural Science Foundation of China","award":["62201063"],"award-info":[{"award-number":["62201063"]}]},{"name":"National Natural Science Foundation of China","award":["4214065"],"award-info":[{"award-number":["4214065"]}]},{"name":"Natural Science Foundation of Beijing Municipality","award":["42192580"],"award-info":[{"award-number":["42192580"]}]},{"name":"Natural Science Foundation of Beijing Municipality","award":["42192584"],"award-info":[{"award-number":["42192584"]}]},{"name":"Natural Science Foundation of Beijing Municipality","award":["62201063"],"award-info":[{"award-number":["62201063"]}]},{"name":"Natural Science Foundation of Beijing Municipality","award":["4214065"],"award-info":[{"award-number":["4214065"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With the development of remote sensing technology, the continuing accumulation of remote sensing data has brought great challenges to the remote sensing field. Although multiple deep-learning-based classification methods have made great progress in scene classification tasks, they are still unable to address the problem of model learning continuously. Facing the constantly updated remote sensing data stream, there is an inevitable problem of forgetting historical information in the model training, which leads to catastrophic forgetting. Therefore, we propose a continual contrastive learning method based on knowledge distillation and contrastive learning in this paper, which is named the Continual Contrastive Learning Network (CCLNet). To overcome the problem of knowledge forgetting, we first designed a knowledge distillation module based on a spatial feature which contains sufficient historical knowledge. The spatial and category-level knowledge distillation enables the model to effectively preserve the already learned knowledge in the current scene classification model. Then, we introduced contrastive learning by leveraging the comparison of augmented samples and minimizing the distance in the feature space to further enhance the extracted feature during the continual learning process. To evaluate the performance of our designed model on streaming remote sensing scene data, we performed three steps of continuous learning experiments on three datasets, the AID, RSI, and NWPU datasets, and simulated the streaming of remote sensing scene data with the aggregate of the three datasets. We also compared other benchmark continual learning models. The experimental results demonstrate that our method achieved superior performance in the continuous scene classification task.<\/jats:p>","DOI":"10.3390\/rs14205105","type":"journal-article","created":{"date-parts":[[2022,10,12]],"date-time":"2022-10-12T22:45:29Z","timestamp":1665614729000},"page":"5105","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Continual Contrastive Learning for Cross-Dataset Scene Classification"],"prefix":"10.3390","volume":"14","author":[{"given":"Rui","family":"Peng","sequence":"first","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3125-2310","authenticated-orcid":false,"given":"Wenzhi","family":"Zhao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaiyuan","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fengcheng","family":"Ji","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caixia","family":"Rong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,12]]},"reference":[{"key":"ref_1","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":"L. Degrad. Dev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"8775","DOI":"10.1109\/TGRS.2019.2922908","article-title":"A Multi-Level Semantic Scene Interpretation Strategy for Change Interpretation in Remote Sensing Imagery","volume":"57","author":"Ghazouani","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1080\/01431161.2012.705443","article-title":"Automatic landslide detection from remote-sensing imagery using a scene classification method based on boVW and pLSA","volume":"34","author":"Cheng","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.isprsjprs.2018.09.014","article-title":"Deep networks under scene-level supervision for multi-class geospatial object detection from remote sensing images","volume":"146","author":"Li","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chen, C., Gong, W., Chen, Y., and Li, W. (2019). Object detection in remote sensing images based on a scene-contextual feature pyramid network. Remote Sens., 11.","DOI":"10.3390\/rs11030339"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"de Lima, R.P., and Marfurt, K. (2020). Convolutional neural network for remote-sensing scene classification: Transfer learning analysis. Remote Sens., 12.","DOI":"10.3390\/rs12234003"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1986","DOI":"10.1109\/JSTARS.2020.2988477","article-title":"Classification of high-spatial-resolution remote sensing scenes method using transfer learning and deep convolutional neural network","volume":"13","author":"Li","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1324","DOI":"10.1109\/LGRS.2019.2896411","article-title":"Domain adaptation for convolutional neural networks-based remote sensing scene classification","volume":"16","author":"Song","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","first-page":"3366","article-title":"A continual learning survey: Defying forgetting in classification tasks","volume":"44","author":"Aljundi","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Rebuffi, S.-A., Kolesnikov, A., Sperl, G., and Lampert, C.H. iCaRL: Incremental Classifier and Representation Learning Sylvestre-Alvise. Proceedings of the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21\u201326 July 2017.","DOI":"10.1109\/CVPR.2017.587"},{"key":"ref_11","unstructured":"Kamra, N., Gupta, U., and Liu, Y. (2017). Deep Generative Dual Memory Network for Continual Learning. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Rostami, M., Kolouri, S., Pilly, P., and McClelland, J. (2020, January 7\u201312). Generative continual concept learning. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i04.6006"},{"key":"ref_13","unstructured":"Shin, H., Lee, J.K., Kim, J., and Kim, J. (2017). Continual learning with deep generative replay. Adv. Neural Inf. Process. Syst., 30."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Verma, V.K., Liang, K.J., Mehta, N., Rai, P., and Carin, L. (2021, January 19\u201325). Efficient feature transformations for discriminative and generative continual learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Virtual.","DOI":"10.1109\/CVPR46437.2021.01365"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3521","DOI":"10.1073\/pnas.1611835114","article-title":"Overcoming catastrophic forgetting in neural networks","volume":"114","author":"James","year":"2017","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Aljundi, R., Babiloni, F., and Elhoseiny, M. (2018, January 8\u201314). Memory Aware Synapses: Learning what ( not ) to forget. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01219-9_9"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.neunet.2019.01.012","article-title":"Continual lifelong learning with neural networks: A review","volume":"113","author":"Parisi","year":"2019","journal-title":"Neural Networks"},{"key":"ref_18","unstructured":"Yoon, J., Yang, E., Lee, J., and Hwang, S.J. (2017). Lifelong Learning with Dynamically Expandable Networks. arXiv."},{"key":"ref_19","unstructured":"Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., and Hadsell, R. (2016). Progressive Neural Networks. arXiv."},{"key":"ref_20","unstructured":"Fernando, C., Banarse, D., Blundell, C., Zwols, Y., Ha, D., Rusu, A.A., Pritzel, A., and Wierstra, D. (2017). Pathnet: Evolution channels gradient descent in super neural networks. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Mallya, A., and Lazebnik, S. (2018, January 18\u201322). PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00810"},{"key":"ref_22","unstructured":"Lee, J. (2021). Co2L: Contrastive Continual Learning. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Luo, Z., Li, J., Chen, C., and Piao, Y. (2020). When self-supervised learning meets scene classification: Remote sensing scene classification based on a multitask learning framework. Remote Sens., 12.","DOI":"10.3390\/rs12203276"},{"key":"ref_24","first-page":"1","article-title":"Remote Sensing Image Scene Classification with Self-Supervised Paradigm Under Limited Labeled Samples","volume":"19","author":"Tao","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Stojni\u0107, V., and Risojevi\u0107, V. (2021, January 19\u201325). Self-Supervised Learning of Remote Sensing Scene Representations Using Contrastive Multiview Coding. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Nashville, TN, USA.","DOI":"10.1109\/CVPRW53098.2021.00129"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2321","DOI":"10.1109\/LGRS.2015.2475299","article-title":"Remote Sensing Scene Classification","volume":"12","author":"Zou","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1109\/LGRS.2015.2499239","article-title":"Using ImageNet Pretrained Networks","volume":"13","author":"Marmanis","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2015","DOI":"10.1109\/JSTARS.2015.2444405","article-title":"Unsupervised Feature Learning Via Spectral Clustering of Multidimensional Patches for Remotely Sensed Scene Classification","volume":"8","author":"Hu","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4775","DOI":"10.1109\/TGRS.2017.2700322","article-title":"Deep Feature Fusion for VHR Remote Sensing Scene Classification","volume":"55","author":"Chaib","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R. (2020, January 13\u201319). Momentum Contrast for Unsupervised Visual Representation Learning. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref_31","unstructured":"Chen, T., Kornblith, S., Norouzi, M., and Hinton, G. (2020, January 13\u201318). A simple framework for contrastive learning of visual representations. Proceedings of the 37th International Conference on Machine Learning, PMLR, Virtual."},{"key":"ref_32","first-page":"22243","article-title":"Big Self-Supervised Models are Strong Semi-Supervised Learners","volume":"33","author":"Chen","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_33","first-page":"9912","article-title":"Unsupervised Learning of Visual Features by Contrasting Cluster Assignments","volume":"33","author":"Caron","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_34","first-page":"21271","article-title":"Bootstrap Your Own Latent\u2014A New Approach to Self-Supervised Learning","volume":"33","author":"Grill","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Chen, X., and Ai, F. (2021, January 19\u201325). Exploring Simple Siamese Representation Learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Virtual.","DOI":"10.1109\/CVPR46437.2021.01549"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"11643","DOI":"10.1109\/JSTARS.2021.3126082","article-title":"MSMatch: Semisupervised Multispectral Scene Classification with Few Labels","volume":"14","author":"Gomez","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_37","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_38","first-page":"1","article-title":"Spatial-Temporal Invariant Contrastive Learning for Remote Sensing Scene Classification","volume":"19","author":"Huang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_39","unstructured":"Hinton, G., Vinyals, O., and Dean, J. (2015). Distilling the knowledge in a neural network. arXiv."},{"key":"ref_40","unstructured":"M\u00fcller, R., Kornblith, S., and Hinton, G. (2019). When does label smoothing help?. Adv. Neural Inf. Process. Syst., 32."},{"key":"ref_41","unstructured":"Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., and Bengio, Y. (2015). FitNets: Hints for thin deep nets. Proc. ICLR, 1\u201313."},{"key":"ref_42","unstructured":"Zagoruyko, S., and Komodakis, N. (2017, January 24\u201326). Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer. Proceedings of the 5th International Conference on Learning Representations, Toulon, France."},{"key":"ref_43","first-page":"7945","article-title":"Show, Attend and Distill: Knowledge Distillation via Attention-based Feature Matching","volume":"35","author":"Ji","year":"2021","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2935","DOI":"10.1109\/TPAMI.2017.2773081","article-title":"Learning without Forgetting","volume":"40","author":"Li","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Castro, F.M., Mar, M.J., and Schmid, C. (2018). End-to-End Incremental Learning Francisco. Proc. Eur. Conf. Comput. Vis., 16\u201318.","DOI":"10.1007\/978-3-030-01258-8_15"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., and Fu, Y. (2019, January 15\u201320). Large scale incremental learning. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00046"},{"key":"ref_47","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_48","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_49","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., and Komodakis, N. (2016, January 19\u201322). Wide Residual Networks. Proceedings of the British Machine Vision Conference (BMVC), York, UK.","DOI":"10.5244\/C.30.87"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.neucom.2021.10.021","article-title":"Online continual learning in image classification: An empirical survey","volume":"469","author":"Mai","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_51","unstructured":"Zenke, F., Poole, B., and Ganguli, S. (2017, January 6\u201311). Continual Learning Through Synaptic Intelligence. Proceedings of the International Conference on Machine Learning, Sydney, Australia."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"E10467","DOI":"10.1073\/pnas.1803839115","article-title":"Alleviating catastrophic forgetting using context- dependent gating and synaptic stabilization","volume":"115","author":"Masse","year":"2018","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_53","first-page":"E2496","article-title":"Note on the quadratic penalties in elastic weight consolidation","volume":"115","year":"2018","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_54","first-page":"2579","article-title":"Visualizing Data using t-SNE","volume":"9","author":"Laurens","year":"2008","journal-title":"J. Mach. Learn. Res."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/20\/5105\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:53:01Z","timestamp":1760143981000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/20\/5105"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,12]]},"references-count":54,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["rs14205105"],"URL":"https:\/\/doi.org\/10.3390\/rs14205105","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,12]]}}}