{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T14:35:30Z","timestamp":1776695730160,"version":"3.51.2"},"reference-count":22,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2018,4,13]],"date-time":"2018-04-13T00:00:00Z","timestamp":1523577600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Super-resolution for satellite video attaches much significance to earth observation accuracy, and the special imaging and transmission conditions on the video satellite pose great challenges to this task. The existing deep convolutional neural-network-based methods require pre-processing or post-processing to be adapted to a high-resolution size or pixel format, leading to reduced performance and extra complexity. To this end, this paper proposes a five-layer end-to-end network structure without any pre-processing and post-processing, but imposes a reshape or deconvolution layer at the end of the network to retain the distribution of ground objects within the image. Meanwhile, we formulate a joint loss function by combining the output and high-dimensional features of a non-linear mapping network to precisely learn the desirable mapping relationship between low-resolution images and their high-resolution counterparts. Also, we use satellite video data itself as a training set, which favors consistency between training and testing images and promotes the method\u2019s practicality. Experimental results on \u201cJilin-1\u201d satellite video imagery show that this method demonstrates a superior performance in terms of both visual effects and measure metrics over competing methods.<\/jats:p>","DOI":"10.3390\/s18041194","type":"journal-article","created":{"date-parts":[[2018,4,13]],"date-time":"2018-04-13T14:38:14Z","timestamp":1523630294000},"page":"1194","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":62,"title":["Super-Resolution for \u201cJilin-1\u201d Satellite Video Imagery via a Convolutional Network"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2956-0613","authenticated-orcid":false,"given":"Aoran","family":"Xiao","sequence":"first","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Multimedia Software, School of Computer, Wuhan University, Wuhan 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yexian","family":"Ren","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,4,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1023\/A:1026501619075","article-title":"Learning low-level vision","volume":"40","author":"Freeman","year":"2000","journal-title":"Int. J. Comput. Vis."},{"key":"ref_2","unstructured":"Chang, H., Yeung, D.-Y., and Xiong, Y. (July, January 27). Super-resolution through neighbor embedding. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004, Washington, DC, USA."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2861","DOI":"10.1109\/TIP.2010.2050625","article-title":"Image super-resolution via sparse representation","volume":"19","author":"Yang","year":"2010","journal-title":"IEEE Trans. Image Process."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1109\/TMM.2016.2601020","article-title":"SRLSP: A Face Image Super-Resolution Algorithm Using Smooth Regression With Local Structure Prior","volume":"19","author":"Jiang","year":"2016","journal-title":"IEEE Trans. Multimed."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3991","DOI":"10.1109\/TCYB.2016.2594184","article-title":"Noise Robust Face Image Super-Resolution through Smooth Sparse Representation","volume":"47","author":"Jiang","year":"2016","journal-title":"IEEE Trans. Cybern."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2178","DOI":"10.1109\/TMM.2014.2364976","article-title":"Fast Single Image Super-Resolution via Self-Example Learning and Sparse Representation","volume":"16","author":"Zhu","year":"2014","journal-title":"IEEE Trans. Multimed."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"13103","DOI":"10.1109\/ACCESS.2017.2717963","article-title":"Robust Face Super-Resolution via Locality-Constrained Low-Rank Representation","volume":"5","author":"Lu","year":"2017","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.sigpro.2016.05.002","article-title":"Image super-resolution: The techniques, applications, and future","volume":"128","author":"Yue","year":"2016","journal-title":"Signal Process."},{"key":"ref_9","first-page":"184","article-title":"Learning a Deep Convolutional Network for Image Super-Resolution","volume":"8689","author":"Dong","year":"2014","journal-title":"Comput. Vis."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","article-title":"Image Super-Resolution Using Deep Convolutional Networks","volume":"38","author":"Dong","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Shi, W., Caballero, J., Husz\u00e1r, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., and Wang, Z. (2016, January 27\u201330). Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.207"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Johnson, J., Alahi, A., and Fei-Fei, L. (2016, January 11\u201314). Perceptual losses for real-time style transfer and super-resolution. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"ref_13","unstructured":"Simonyan, K., and Zisserman, A. (arXiv, 2014). Very Deep Convolutional Networks for Large-Scale Image Recognition, arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Husz\u00e1r, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., and Wang, Z. (arXiv, 2016). Photo-realistic single image super-resolution using a generative adversarial network, arXiv.","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ren, H., El-Khamy, M., and Lee, J. (2017, January 21\u201326). Image Super Resolution Based on Fusing Multiple Convolution Neural Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.142"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Dahl, R., Norouzi, M., and Shlens, J. (2017, January 22\u201329). Pixel Recursive Super Resolution. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.581"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Lai, W.-S., Huang, J.-B., Ahuja, N., and Yang, M.-H. (2017, January 21\u201326). Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.618"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Bosch, M., Gifford, C.M., and Rodriguez, P.A. (arXiv, 2017). Super-Resolution for Overhead Imagery Using DenseNets and Adversarial Learning, arXiv.","DOI":"10.1109\/WACV.2018.00159"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Caballero, J., Ledig, C., Aitken, A., Acosta, A., Totz, J., Wang, Z., and Shi, W. (2017, January 21\u201326). Real-time video super-resolution with spatio-temporal networks and motion compensation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.304"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Tao, X., Gao, H., Liao, R., Wang, J., and Jia, J. (2017, January 22\u201329). Detail-Revealing Deep Video Super-Resolution. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.479"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhou, W., Newsam, S., Li, C., and Shao, Z. (2017). Learning Low Dimensional Convolutional Neural Networks for High-Resolution Remote Sensing Image Retrieval. Remote Sens., 9.","DOI":"10.3390\/rs9050489"},{"key":"ref_22","unstructured":"Kingma, D., and Ba, J. (arXiv, 2014). Adam: A Method for Stochastic Optimization, arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/4\/1194\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:00:38Z","timestamp":1760194838000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/4\/1194"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,4,13]]},"references-count":22,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2018,4]]}},"alternative-id":["s18041194"],"URL":"https:\/\/doi.org\/10.3390\/s18041194","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,4,13]]}}}