{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T12:09:25Z","timestamp":1768738165160,"version":"3.49.0"},"reference-count":35,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,3,15]],"date-time":"2021-03-15T00:00:00Z","timestamp":1615766400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100012542","name":"Sichuan Province Science and Technology Support Program","doi-asserted-by":"publisher","award":["2020YFG0306"],"award-info":[{"award-number":["2020YFG0306"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Aerospace"],"abstract":"<jats:p>High-resolution remote sensing images are the key data source for the visual system of a flight simulator for training a qualified pilot. However, due to hardware limitations, it is an expensive task to collect spectral and spatial images at very high resolutions. In this work, we try to tackle this issue with another perspective based on image super-resolution (SR) technology. First, we present a new ultra-high-resolution remote sensing image dataset named Airport80, which is captured from the airspace near various airports. Second, a deep learning baseline is proposed by applying the generative and adversarial mechanism, which is able to reconstruct a high-resolution image during a single image super-resolution. Experimental results for our benchmark demonstrate the effectiveness of the proposed network and show it has reached satisfactory performances.<\/jats:p>","DOI":"10.3390\/aerospace8030076","type":"journal-article","created":{"date-parts":[[2021,3,15]],"date-time":"2021-03-15T11:38:58Z","timestamp":1615808338000},"page":"76","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Remote Sensing Image Super-Resolution for the Visual System of a Flight Simulator: Dataset and Baseline"],"prefix":"10.3390","volume":"8","author":[{"given":"Wenyi","family":"Ge","sequence":"first","affiliation":[{"name":"National Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer Science, Sichuan University, Chengdu 610000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhitao","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing Satellite Navigation Center (BSNC), Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guigui","family":"Wang","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer Science, Sichuan University, Chengdu 610000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shihan","family":"Tan","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer Science, Sichuan University, Chengdu 610000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianwei","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer Science, Sichuan University, Chengdu 610000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Lin, Y., Deng, L., Chen, Z., Wu, X., Zhang, J., and Yang, B. (2019). A Real-Time ATC Safety Monitoring Framework Using a Deep Learning Approach. IEEE Trans. Intell. Transp. Syst., 4572\u20134581.","DOI":"10.1109\/TITS.2019.2940992"},{"key":"ref_2","unstructured":"Lin, Y., Guo, D., Zhang, J., Chen, Z., and Yang, B. (2020). A Unified Framework for Multilingual Speech Recognition in Air Traffic Control Systems. IEEE Trans. Neural Netw. Learn. Syst., 1\u201313."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"105628","DOI":"10.1016\/j.aap.2020.105628","article-title":"Automated traffic incident detection with a smaller dataset based on generative adversarial networks","volume":"144","author":"Lin","year":"2020","journal-title":"Accid. Anal. Prev."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Li, L., Lin, Y., Du, B., Yang, F., and Ran, B. (2020). Real-time traffic incident detection based on a hybrid deep learning model. Transportmetrica, 1\u201321.","DOI":"10.1080\/23249935.2020.1813214"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Huszar, F., Caballero, J., Aitken, P.A., Tejani, A., Totz, J., Wang, Z., and Shi, W. (2017, January 21\u201326). Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. Proceedings of the CVPR, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 7\u201313). Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. Proceedings of the ICCV, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Xu, X., Xiong, X., Wang, J., and Li, X. (2020, January 23\u201328). Deformable Kernel Convolutional Network for Video Extreme Super-Resolution. Proceedings of the ECCV Workshops, Glasgow, UK.","DOI":"10.1007\/978-3-030-66823-5_5"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1109\/38.988747","article-title":"Example-Based Super-Resolution","volume":"22","author":"Freeman","year":"2002","journal-title":"IEEE Comput. Graph. Appl."},{"key":"ref_9","unstructured":"Chang, H., Yeung, D.Y., and Xiong, Y. (July, January 27). Super-resolution through neighbor embedding. Proceedings of the CVPR, Washington, DC, USA."},{"key":"ref_10","unstructured":"Yang, J., Wright, J., Huang, S.T., and Ma, Y. (2008, January 24\u201326). Image super-resolution as sparse representation of raw image patches. Proceedings of the CVPR, Anchorage, AK, USA."},{"key":"ref_11","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":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, K.J., and Lee, M.K. (2016, January 27\u201330). Accurate Image Super-Resolution Using Very Deep Convolutional Networks. Proceedings of the CVPR, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.182"},{"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 CVPR, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Tai, Y., Yang, J., and Liu, X. (2017, January 21\u201326). Image Super-Resolution via Deep Recursive Residual Network. Proceedings of the CVPR, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.298"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Shi, W., Caballero, J., Husz\u00e1r, F., Totz, J., Aitken, P.A., 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 CVPR, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.207"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., and Lee, M.K. (2017, January 21\u201326). Enhanced Deep Residual Networks for Single Image Super-Resolution. Proceedings of the CVPR Workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.151"},{"key":"ref_17","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, Lille, France."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tian, Y., Kong, Y., Zhong, B., and Fu, Y. (2018, January 24\u201326). Residual Dense Network for Image Super-Resolution. Proceedings of the CVPR, Anchorage, AK, USA.","DOI":"10.1109\/CVPR.2018.00262"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Johnson, J., Alahi, A., and Fei-Fei, L. (2016, January 8\u201316). 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_20","doi-asserted-by":"crossref","unstructured":"Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Loy, C.C., Qiao, Y., and Tang, X. (2018, January 18\u201323). ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks. Proceedings of the Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"ref_21","unstructured":"Lugmayr, A., Danelljan, M., and Timofte, R. (November, January 27). Unsupervised Learning for Real-World Super-Resolution. Proceedings of the ICCV Workshops, Seoul, Korea."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lugmayr, A., Joon, H.N., Won, S.Y., Kim, G., Kwon, D., Hsu, C.C., Lin, C.H., Huang, Y., Sun, X., and Lu, W. (November, January 27). AIM 2019 Challenge on Real-World Image Super-Resolution\u2014Methods and Results. Proceedings of the ICCV Workshops, Seoul, Korea.","DOI":"10.1109\/ICCVW.2019.00442"},{"key":"ref_23","unstructured":"Fritsche, M., Gu, S., and Timofte, R. (November, January 27). Frequency Separation for Real-World Super-Resolution. Proceedings of the ICCV Workshops, Seoul, Korea."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Bevilacqua, M., Roumy, A., Guillemot, C., and Alberi-Morel, M.L. (2012, January 3\u20137). Low-Complexity Single-Image Super-Resolution based on Nonnegative Neighbor Embedding. Proceedings of the BMVC, Surrey, UK.","DOI":"10.5244\/C.26.135"},{"key":"ref_25","unstructured":"Zeyde, R., Elad, M., and Protter, M. (2010). On single image scale-up using sparse-representations. Curves and Surfaces, Springer."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Huang, J.B., Singh, A., and Ahuja, N. (2015, January 7\u201312). Single image super-resolution from transformed self-exemplars. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299156"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: from error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_28","unstructured":"Goodfellow, J.I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, C.A., and Bengio, Y. (2014, January 8\u201313). Generative Adversarial Nets. Proceedings of the Advances in Neural Information Processing Systems 27 (NIPS 2014), Montreal, QC, Canada."},{"key":"ref_29","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_30","unstructured":"Yu, F., and Koltun, V. (2016, January 2\u20134). Multi-Scale Context Aggregation by Dilated Convolutions. Proceedings of the International Conference on Learning Representations, San Juan, Puerto Rico."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., and Wei, Y. (2017, January 22\u201329). Deformable Convolutional Networks. Proceedings of the ICCV, Venice, Italy.","DOI":"10.1109\/ICCV.2017.89"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_33","unstructured":"Jolicoeur-Martineau, A. (2019, January 6\u20139). The relativistic discriminator: A key element missing from standard GAN. Proceedings of the International Conference on Learning Representations, New Orleans, LA, USA."},{"key":"ref_34","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wang, Z., Chen, J., and Hoi, C.H.S. (2020). Deep Learning for Image Super-resolution: A Survey. IEEE Trans. Pattern Anal. Mach. 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