{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T20:53:04Z","timestamp":1774126384327,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2019,7,4]],"date-time":"2019-07-04T00:00:00Z","timestamp":1562198400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61502354, 61671332, 41501505, 61771353,"],"award-info":[{"award-number":["61502354, 61671332, 41501505, 61771353,"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003819","name":"Natural Science Foundation of Hubei Province","doi-asserted-by":"publisher","award":["2012FFA099, 2012FFA134, 2013CF125, 2014CFA130, 2015CFB451"],"award-info":[{"award-number":["2012FFA099, 2012FFA134, 2013CF125, 2014CFA130, 2015CFB451"]}],"id":[{"id":"10.13039\/501100003819","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Recently, the application of satellite remote sensing images is becoming increasingly popular, but the observed images from satellite sensors are frequently in low-resolution (LR). Thus, they cannot fully meet the requirements of object identification and analysis. To utilize the multi-scale characteristics of objects fully in remote sensing images, this paper presents a multi-scale residual neural network (MRNN). MRNN adopts the multi-scale nature of satellite images to reconstruct high-frequency information accurately for super-resolution (SR) satellite imagery. Different sizes of patches from LR satellite images are initially extracted to fit different scale of objects. Large-, middle-, and small-scale deep residual neural networks are designed to simulate differently sized receptive fields for acquiring relative global, contextual, and local information for prior representation. Then, a fusion network is used to refine different scales of information. MRNN fuses the complementary high-frequency information from differently scaled networks to reconstruct the desired high-resolution satellite object image, which is in line with human visual experience (\u201clook in multi-scale to see better\u201d). Experimental results on the SpaceNet satellite image and NWPU-RESISC45 databases show that the proposed approach outperformed several state-of-the-art SR algorithms in terms of objective and subjective image qualities.<\/jats:p>","DOI":"10.3390\/rs11131588","type":"journal-article","created":{"date-parts":[[2019,7,4]],"date-time":"2019-07-04T11:13:18Z","timestamp":1562238798000},"page":"1588","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":119,"title":["Satellite Image Super-Resolution via Multi-Scale Residual Deep Neural Network"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8117-2012","authenticated-orcid":false,"given":"Tao","family":"Lu","sequence":"first","affiliation":[{"name":"Hubei Key Laboratory of Intelligent Robot, School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430205, China"}]},{"given":"Jiaming","family":"Wang","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Intelligent Robot, School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430205, China"}]},{"given":"Yanduo","family":"Zhang","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Intelligent Robot, School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430205, China"}]},{"given":"Zhongyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan 430072, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5694-505X","authenticated-orcid":false,"given":"Junjun","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,4]]},"reference":[{"key":"ref_1","first-page":"317","article-title":"Multiframe image restoration and registration","volume":"1","author":"Tsai","year":"1984","journal-title":"Adv. Comput. Vis. Image Process."},{"key":"ref_2","unstructured":"Lim, K.H., and Kwoh, L.K. (2009, January 18\u201323). Super-resolution for SPOT5\u2014Beyond supermode. Proceedings of the 30th Asian Conference on Remote Sensing, Beijing, China."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1423","DOI":"10.1007\/s00138-014-0623-4","article-title":"Super-resolution: A comprehensive survey","volume":"25","author":"Nasrollahi","year":"2014","journal-title":"Mach. Vis. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Garzelli, A. (2016). A Review of Image Fusion Algorithms Based on the Super-Resolution Paradigm. Remote Sens., 8.","DOI":"10.3390\/rs8100797"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1656","DOI":"10.1109\/JSTARS.2018.2805923","article-title":"Remote Sensing Image Fusion with Deep Convolutional Neural Network","volume":"11","author":"Shao","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1109\/TIP.2006.888334","article-title":"A MAP approach for joint motion estimation, segmentation, and super resolution","volume":"16","author":"Shen","year":"2007","journal-title":"IEEE Trans. Image Process."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1306","DOI":"10.1109\/TSMCB.2012.2189561","article-title":"Remote sensing image subpixel mapping based on adaptive differential evolution","volume":"42","author":"Zhong","year":"2012","journal-title":"IEEE Trans. Syst. Man Cybern. Part B"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1109\/TCI.2016.2516909","article-title":"Robust Multiframe Super-Resolution Employing Iteratively Re-Weighted Minimization","volume":"2","author":"Kohler","year":"2016","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1109\/TNN.2010.2089470","article-title":"Super-resolution method for face recognition using nonlinear mappings on coherent features","volume":"22","author":"Huang","year":"2011","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1109\/TCI.2016.2629284","article-title":"RAISR: Rapid and accurate image super resolution","volume":"3","author":"Romano","year":"2017","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1114","DOI":"10.1109\/TGRS.2013.2243736","article-title":"Support vector regression-based downscaling for intercalibration of multiresolution satellite images","volume":"51","author":"Zhang","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1109\/TCI.2017.2691554","article-title":"Fast Single-Image Super-Resolution Via Tangent Space Learning of High-Resolution-Patch Manifold","volume":"3","author":"Dang","year":"2017","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1109\/LGRS.2008.915935","article-title":"Superresolution Construction of Multispectral Imagery Based on Local Enhancement","volume":"5","author":"Elbakary","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1648","DOI":"10.1109\/TNNLS.2013.2262001","article-title":"Single image super-resolution with multiscale similarity learning","volume":"24","author":"Zhang","year":"2013","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1109\/TCI.2016.2532323","article-title":"Video Super-Resolution with Convolutional Neural Networks","volume":"2","author":"Kappeler","year":"2016","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_17","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_18","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_19","doi-asserted-by":"crossref","unstructured":"Kim, J., Kwon Lee, J., and Mu Lee, K. (2016, January 27\u201330). Accurate image super-resolution using very deep convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.182"},{"key":"ref_20","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 superresolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.618"},{"key":"ref_21","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. (2017, January 21\u201326). Photo-realistic single image super-resolution using a generative adversarial network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref_22","first-page":"1","article-title":"Edge-Enhanced GAN for Remote Sensing Image Superresolution","volume":"19","author":"Jiang","year":"2019","journal-title":"IEEE Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2398","DOI":"10.1109\/LGRS.2017.2766204","article-title":"Video Satellite Imagery Super Resolution via Convolutional Neural Networks","volume":"14","author":"Luo","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2530","DOI":"10.1109\/TIP.2018.2887017","article-title":"Multi-Memory Convolutional Neural Network for Video Super-Resolution","volume":"28","author":"Wang","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lu, T., Ming, D., Lin, X., Hong, Z., Bai, X., and Fang, J. (2018). Detecting building edges from high spatial resolution remote sensing imagery using richer convolution features network. Remote Sens., 10.","DOI":"10.3390\/rs10091496"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhao, P., Liu, K., Zou, H., and Zhen, X. (2018). Multi-stream convolutional neural network for SAR automatic target recognition. Remote Sens., 10.","DOI":"10.3390\/rs10091473"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhang, W., Witharana, C., Liljedahl, A., and Kanevskiy, M. (2018). Deep convolutional neural networks for automated characterization of arctic ice-wedge polygons in very high spatial resolution aerial imagery. Remote Sens., 10.","DOI":"10.3390\/rs10091487"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Xu, Y., Zhu, M., Li, S., Feng, H., Ma, S., and Che, J. (2018). End-to-end airport detection in remote sensing images combining cascade region proposal networks and multi-threshold detection networks. Remote Sens., 10.","DOI":"10.3390\/rs10101516"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1007\/s11263-018-1117-z","article-title":"Locality preserving matching","volume":"127","author":"Ma","year":"2019","journal-title":"Int. J. Comput. Vis."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Liu, J., Bai, W., and Guo, Z. (2014, January 27\u201330). Exploiting multi-scale spatial structures for sparsity based single image super-resolution. Proceedings of the 2014 IEEE International Conference on Image Processing, Paris, France.","DOI":"10.1109\/ICIP.2014.7025787"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Fu, J., Zheng, H., and Mei, T. (2017, January 21\u201326). Look Closer to See Better: Recurrent Attention Convolutional Neural Network for Fine-Grained Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.476"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Liu, Y., Cheng, M., Hu, X., Wang, K., and Bai, X. (2017, January 21\u201326). Richer Convolutional Features for Edge Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.622"},{"key":"ref_34","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, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Du, X., Qu, X., He, Y., and Guo, D. (2018). Single Image Super-Resolution Based on Multi-Scale Competitive Convolutional Neural Network. Sensors, 18.","DOI":"10.3390\/s18030789"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhang, X., Yang, W., Hu, Y., and Liu, J. (2018, January 7\u201310). DMCNN: Dual-Domain Multi-Scale Convolutional Neural Network for Compression Artifacts Removal. Proceedings of the 2018 25th IEEE International Conference on Image Processing, Athens, Greece.","DOI":"10.1109\/ICIP.2018.8451694"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zjournaleiler, M.D., and Fergus, R. (2014). Visualizing and Understanding Convolutional Networks, Springer.","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1167\/15.7.3","article-title":"Low-level properties of natural images predict topographic patterns of neural response in the ventral visual pathway","volume":"15","author":"Andrews","year":"2015","journal-title":"J. Vis."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.image.2019.03.019","article-title":"Face super-resolution via bilayer contextual representation","volume":"75","author":"Zeng","year":"2019","journal-title":"Front. Signal Process. Image Commun."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"93","DOI":"10.3389\/fncom.2014.00093","article-title":"Hierarchical representation of shapes in visual cortex from localized features to figural shape segregation","volume":"8","author":"Tschechne","year":"2014","journal-title":"Front. Comput. Neurosci."},{"key":"ref_41","first-page":"1","article-title":"Category-based deep CCA for fine-grained venue discovery from multimodal data","volume":"99","author":"Yu","year":"2018","journal-title":"Front. IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_42","first-page":"20","article-title":"Deep cross-modal correlation learning for audio and lyrics in music retrieval","volume":"15","author":"Yu","year":"2019","journal-title":"Front. ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Achanta, R., Hemami, S., Estrada, F., and Susstrunk, S. (2009, January 20\u201326). Frequency-tuned salient region detection. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, Miami Beach, FL, USA.","DOI":"10.1109\/CVPRW.2009.5206596"},{"key":"ref_44","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_45","doi-asserted-by":"crossref","unstructured":"Huang, J., 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_46","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_47","doi-asserted-by":"crossref","first-page":"2378","DOI":"10.1109\/TIP.2011.2109730","article-title":"FSIM: A Feature Similarity Index for Image Quality Assessment","volume":"20","author":"Zhang","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_48","unstructured":"Sheikh, H.R., and Bovik, A.C. (2004, January 17\u201321). Image information and visual quality. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Montreal, QC, Canada."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Lu, T., Wang, J., Zhou, H., Jiang, J., Ma, J., and Wang, Z. (2018). Rectangular-Normalized Superpixel Entropy Index for Image Quality Assessment. Entropy, 20.","DOI":"10.3390\/e20120947"},{"key":"ref_50","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 IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T. (2014, January 3\u20137). Caffe: Convolutional architecture for fast feature embedding. Proceedings of the 22nd ACM international conference on Multimedia, Orlando, FL, USA.","DOI":"10.1145\/2647868.2654889"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1086","DOI":"10.1109\/TIP.2017.2768185","article-title":"How Does the Low-Rank Matrix Decomposition Help Internal and External Learnings for Super-Resolution","volume":"27","author":"Wang","year":"2018","journal-title":"IEEE Trans. Image Process."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/13\/1588\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:02:30Z","timestamp":1760187750000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/13\/1588"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,4]]},"references-count":52,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2019,7]]}},"alternative-id":["rs11131588"],"URL":"https:\/\/doi.org\/10.3390\/rs11131588","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,4]]}}}