{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T05:14:48Z","timestamp":1780550088828,"version":"3.54.1"},"reference-count":43,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,2,4]],"date-time":"2020-02-04T00:00:00Z","timestamp":1580774400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Nature Science Foundation of China","award":["91738302"],"award-info":[{"award-number":["91738302"]}]},{"name":"National Nature Science Foundation of China","award":["61671336"],"award-info":[{"award-number":["61671336"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Real-time transmission of satellite video data is one of the fundamentals in the applications of video satellite. Making use of the historical information to eliminate the long-term background redundancy (LBR) is considered to be a crucial way to bridge the gap between the compressed data rate and the bandwidth between the satellite and the Earth. The main challenge lies in how to deal with the variant image pixel values caused by the change of shooting conditions while keeping the structure of the same landscape unchanged. In this paper, we propose a representation learning based method to model the complex evolution of the landscape appearance under different conditions by making use of the historical image series. Under this representation model, the image is disentangled into the content part and the style part. The former represents the consistent landscape structure, while the latter represents the conditional parameters of the environment. To utilize the knowledge learned from the historical image series, we generate synthetic reference frames for the compression of video frames through image translation by the representation model. The synthetic reference frames can highly boost the compression efficiency by changing the original intra-frame prediction to inter-frame prediction for the intra-coded picture (I frame). Experimental results show that the proposed representation learning-based compression method can save an average of 44.22% bits over HEVC, which is significantly higher than that using references generated under the same conditions. Bitrate savings reached 18.07% when applied to satellite video data with arbitrarily collected reference images.<\/jats:p>","DOI":"10.3390\/rs12030497","type":"journal-article","created":{"date-parts":[[2020,2,5]],"date-time":"2020-02-05T03:18:48Z","timestamp":1580872728000},"page":"497","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Learned Representation of Satellite Image Series for Data Compression"],"prefix":"10.3390","volume":"12","author":[{"given":"Liang","family":"Liao","sequence":"first","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan 430072, China"},{"name":"National Institute of Informatics, Tokyo 101-8430, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0833-5679","authenticated-orcid":false,"given":"Jing","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan 430072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yating","family":"Li","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Multimedia Software, Wuhan 430072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mi","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan 430072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruimin","family":"Hu","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Multimedia Software, Wuhan 430072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1858","DOI":"10.1109\/TCSVT.2012.2223052","article-title":"Overview of HEVC high-level syntax and reference picture management","volume":"22","author":"Sjoberg","year":"2012","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Xiao, J., Zhu, R., Hu, R., Wang, M., Zhu, Y., Chen, D., and Li, D. (2018). Towards Real-Time Service from Remote Sensing: Compression of Earth Observatory Video Data via Long-Term Background Referencing. Remote Sens., 10.","DOI":"10.3390\/rs10060876"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1109\/MCSE.2014.52","article-title":"IK-SVD: dictionary learning for spatial big data via incremental atom update","volume":"16","author":"Wang","year":"2014","journal-title":"Comput. Sci. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3433","DOI":"10.1007\/s11227-016-1652-8","article-title":"G-IK-SVD: parallel IK-SVD on GPUs for sparse representation of spatial big data","volume":"73","author":"Song","year":"2017","journal-title":"J. Supercomput."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ke, H., Chen, D., Shi, B., Zhang, J., Liu, X., Zhang, X., and Li, X. (2019). Improving Brain E-health Services via High-Performance EEG Classification with Grouping Bayesian Optimization. IEEE Trans. Serv. Comput.","DOI":"10.1109\/TSC.2019.2962673"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ke, H., Chen, D., Shah, T., Liu, X., Zhang, X., Zhang, L., and Li, X. (2018). Cloud-aided online EEG classification system for brain healthcare: A case study of depression evaluation with a lightweight CNN. Software Pract. Exper.","DOI":"10.1002\/spe.2668"},{"key":"ref_7","unstructured":"Jing, X.Y., Zhu, X., Wu, F., You, X., Liu, Q., Yue, D., Hu, R., and Xu, B. (2015, January 8\u201310). Super-resolution person re-identification with semi-coupled low-rank discriminant dictionary learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.patcog.2015.08.012","article-title":"Multi-view low-rank dictionary learning for image classification","volume":"50","author":"Wu","year":"2016","journal-title":"Pattern Recognit."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Liu, X., Tao, X., and Ge, N. (2015, January 11\u201314). Remote-sensing image compression using priori-information and feature registration. Proceedings of the 2015 IEEE 82nd Vehicular Technology Conference (VTC2015-Fall), Glasgow, Scotland.","DOI":"10.1109\/VTCFall.2015.7391115"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Tao, X., Li, S., Zhang, Z., Liu, X., Wang, J., and Lu, J. (2017, January 4\u20137). Prior-Information-Based Remote Sensing Image Compression with Bayesian Dictionary Learning. Proceedings of the 2017 IEEE 85th Vehicular Technology Conference (VTC Spring), Sydney, Australia.","DOI":"10.1109\/VTCSpring.2017.8108417"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1445","DOI":"10.1109\/LSP.2018.2862145","article-title":"Virtual background reference frame based satellite video coding","volume":"25","author":"Wang","year":"2018","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Huang, X., Liu, M.Y., Belongie, S., and Kautz, J. (2018, January 8\u201314). Multimodal unsupervised image-to-image translation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01219-9_11"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., and Aila, T. (2019, January 16\u201320). A style-based generator architecture for generative adversarial networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00453"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Lee, H.Y., Tseng, H.Y., Huang, J.B., Singh, M., and Yang, M.H. (2018, January 8\u201314). Diverse image-to-image translation via disentangled representations. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01246-5_3"},{"key":"ref_15","unstructured":"Chen, D., Tang, Y., Zhang, H., Wang, L., and Li, X. (2019). Incremental factorization of big time series data with blind factor approximation. IEEE Trans. Knowl. Data Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.neucom.2018.08.045","article-title":"Bayesian tensor factorization for multi-way analysis of multi-dimensional EEG","volume":"318","author":"Tang","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"560","DOI":"10.1109\/TCSVT.2003.815165","article-title":"Overview of the H. 264\/AVC video coding standard","volume":"13","author":"Wiegand","year":"2003","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_18","unstructured":"Corporation, S.I. (2019, December 28). SkySat-C Generation Satellite Sensors. Available online: https:\/\/www.satimagingcorp.com\/satellite-sensors\/skysat-1\/."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1109\/TMM.2013.2239629","article-title":"Cloud-based image coding for mobile devices\u2014Toward thousands to one compression","volume":"15","author":"Yue","year":"2013","journal-title":"IEEE Trans. Multimed."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Shi, Z., Sun, X., and Wu, F. (2013, January 15\u201319). Feature-based image set compression. Proceedings of the 2013 IEEE International Conference on Multimedia and Expo (ICME), San Jose, CA, USA.","DOI":"10.1109\/ICME.2013.6607570"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2684","DOI":"10.1109\/TIP.2016.2551366","article-title":"Lossless compression of JPEG coded photo collections","volume":"25","author":"Wu","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"908","DOI":"10.1109\/TMM.2016.2645398","article-title":"Joint compression of near-duplicate Videos","volume":"19","author":"Wang","year":"2016","journal-title":"IEEE Trans. Multimed."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1926","DOI":"10.1109\/TCSVT.2015.2416562","article-title":"Cloud-based distributed image coding","volume":"25","author":"Song","year":"2015","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1691","DOI":"10.1109\/TMM.2016.2581590","article-title":"Knowledge-based coding of objects for multisource surveillance video data","volume":"18","author":"Xiao","year":"2016","journal-title":"IEEE Trans. Multimed."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1007\/s10586-015-0434-z","article-title":"Exploiting global redundancy in big surveillance video data for efficient coding","volume":"18","author":"Xiao","year":"2015","journal-title":"Clust. Comput."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"14705","DOI":"10.1007\/s11042-018-6825-4","article-title":"Multisource surveillance video data coding with hierarchical knowledge library","volume":"78","author":"Chen","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"102685","DOI":"10.1016\/j.jvcir.2019.102685","article-title":"Multisource surveillance video coding with synthetic reference frame","volume":"65","author":"Chen","year":"2019","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Sanchez, E., Serrurier, M., and Ortner, M. (2019). Learning Disentangled Representations of Satellite Image Time Series. arXiv.","DOI":"10.1007\/978-3-030-46133-1_19"},{"key":"ref_29","unstructured":"Gonzalez-Garcia, A., van de Weijer, J., and Bengio, Y. (2018, January 2\u20138). Image-to-image translation for cross-domain disentanglement. Proceedings of the Advances in Neural Information Processing Systems (NIPS), Montr\u00e9al, QC, Canada."},{"key":"ref_30","unstructured":"Zhu, J.Y., Park, T., Isola, P., and Efros, A.A. (Novemver, January 27). Unpaired image-to-image translation using cycle-consistent adversarial networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy."},{"key":"ref_31","unstructured":"Zhu, J.Y., Zhang, R., Pathak, D., Darrell, T., Efros, A.A., Wang, O., and Shechtman, E. (2017). Toward multimodal image-to-image translation. Advances in Neural Information Processing Systems, Neural Information Processing Systems Foundation, Inc."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Auli-Llinas, F., Marcellin, M.W., Sanchez, V., Serra-Sagrista, J., Bartrina-Rapesta, J., and Blanes, I. (April, January 29). Coding scheme for the transmission of satellite imagery. Proceedings of the 2016 Data Compression Conference (DCC), Snowbird, UT, USA.","DOI":"10.1109\/DCC.2016.29"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"5083","DOI":"10.1109\/TGRS.2018.2806082","article-title":"Dual link image coding for earth observation satellites","volume":"56","author":"Marcellin","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1109\/TIP.2013.2294549","article-title":"Background-modeling-based adaptive prediction for surveillance video coding","volume":"23","author":"Zhang","year":"2013","journal-title":"IEEE Trans. Image Process."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1109\/38.946629","article-title":"Color transfer between images","volume":"21","author":"Reinhard","year":"2001","journal-title":"IEEE Comput. Graph. Appl."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., and Efros, A.A. (2017, January 21\u201326). Image-to-image translation with conditional adversarial networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wang, X., Hu, R., Wang, Z., Xiao, J., and Satoh, S. (2019). Long-Term Background Redundancy Reduction for Earth Observatory Video Coding. IEEE Trans. Circuits Syst. Video Technol.","DOI":"10.1109\/TCSVT.2019.2950113"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MC.1984.1659158","article-title":"A technique for high-performance data compression","volume":"6","author":"Welch","year":"1984","journal-title":"Computer"},{"key":"ref_39","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_40","unstructured":"Huang, X., and Belongie, S. (Novemver, January 27). Arbitrary style transfer in real-time with adaptive instance normalization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ros, G., Sellart, L., Materzynska, J., Vazquez, D., and Lopez, A.M. (2016, January 27\u201330). The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.352"},{"key":"ref_42","unstructured":"Institute, F.H.H. (2019, December 28). High Efficiency Video Coding (HEVC). Available online: https:\/\/hevc.hhi.fraunhofer.de\/."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Li, Y., Liu, M., Li, X., Yang, M.-H., and Kautz, J. (2018, January 8\u201314). A closed-form solution to photorealistic image stylization. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01219-9_28"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/3\/497\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T08:54:32Z","timestamp":1760172872000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/3\/497"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,4]]},"references-count":43,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["rs12030497"],"URL":"https:\/\/doi.org\/10.3390\/rs12030497","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,4]]}}}